Scientific direction Development of key enabling technologies
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PhD : selection by topics

Technological challenges >> Nuclear physics
138 proposition(s).

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embedded elapsed-time attestation

Département Systèmes (LETI)

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-10-2021

SL-DRT-21-0089

christine.hennebert@cea.fr

Cyber security : hardware and sofware (.pdf)

With the emergence of a protocol that secures a history of transactions on a peer-to-peer network, Bitcoin introduced the first decentralized digital currency in 2009. The security of the Bitcoin protocol is based on the proof of work and common rules and procedures among peers in the network who participate in the consensus, i.e. the choice of the next block of data to be added to the shared and replicated ledger. The proof of work has two major drawbacks. On the one hand, it ensures security by design by requiring nodes participating in the consensus to work with a computing intensity corresponding to the maximum of Moore's law, which is obviously very energy consuming. On the other hand, the parallelization of this proof process with an implementation in ASICs makes the system vulnerable to Sybil-type attacks by recentralizing resources. The mining pools exploit this vulnerability. The present thesis topic aims at building a proof for embedded and resource constrained objects, which ensures the security of a transaction history at low power. The work will focus on the embedded implementation of the proof mechanism on a system-on-module platform using a TPM 2.0 (Trusted Platform Module) security hardware component as root-of-trust. The solution introduced will have to be robust to the above-mentioned drawbacks and vulnerabilities.

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Fault injection and integrity of edge neural networks: attacks, protections, evaluation

Département Systèmes (LETI)

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-02-2021

SL-DRT-21-0159

pierre-alain.moellic@cea.fr

Artificial intelligence & Data intelligence (.pdf)

One of the major trends of Artificial Intelligence is the large-scale deployment of Machine Learning systems to a large variety of embedded platforms. A lot of semi-conductor practioners propose "A.I. suitable" products, majoritarely with neural networks for inference purpose. The security of the embedded models is a major issue for the deployment of these systems. Several works raised threats such as the adversarial examples or the membership inference attacks with disastrous impact. These works consider the ML aglorithms through a pure algorithmic point of view without taking into consideration the specificities of their physical implementation. Moreover, advanced works are compulsory for physical attacks (i.e., side-channel and fault injection analysis). By considering a overall attack surface gathering the theoretical (i.e. algorithmic) and physical facets, this subject propose to analyze Fault Injection Analysis threats (FIA) targeting the integrity of the model (fooling a prediction) of embedded machine learning systems and the development of appropriate protections. Several works have studied physical attacks for embedded neural networks but with usually naive model architecture on 'simple' 8-bit microcontrolers, or FPGA or at a pure simulation level. These works do not try to link the fault models or the leakages with well-known algorithmic threats. Being based on the experience on other critical systems (e.g., cryptographic primitive), the main idea of this PhD subject will be to jointly analysis the algorithmic and physical world in order to better understand the complexity of the threats and develop efficient defense schemes. The works will answer the following scientific challenges: (1) Caracterization and exploitation of fault models: how to exploit fault injection mechanisms (laser, EM, glitching) to fool the prediction of a model with minimal perturbations. (2) Evaluation of the relevance of classical countermeasures (such as redundancy-based techniques) for this kind of systems and threats. (3) Develop new protections suitable to embedded neural networks.

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Autonomous network management solution for deterministic networks using Artificial Intelligence (AI) techniques

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Systèmes Communiquants

01-02-2021

SL-DRT-21-0178

siwar.benhadjsaid@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

The objective of the thesis is to explore and evaluate the gain that could bring the Artificial Intelligence (AI) techniques to the network management solutions for deterministic networks. The goal is to help deterministic networks to ensure the preservation of the quality of service (QoS) during the routing of end-to-end data flow no matter what changes are made to the network. This will allow to design an autonomous network management solution that is able to configure deterministic networks in the most appropriate way and adapt the configuration when needed (e.g. new terminal connecting to the network, unexpected high latency for certain critical flows, change of the topology caused by the reorganization / reconfiguration of components of the production chain in the factory etc.). This solution will use artificial intelligence methods to learn from experience the conditions that lead to non-compliance with application flow requirements (high latency, low bandwidth, etc.). Learning takes place to recognize, upstream, the situations that may lead to non-compliance with the constraints of application flows and also to predict the effects of changes in input data (new terminal, reorganization of the plant, etc.) on the level of QoS provided to flows in transit. Based on such knowledge, the solution will anticipate QoS degradation situations and, consequently, will decide and push the adequate network reconfiguration which will make it possible to respect the constraints associated with each application flow.

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Distributed resource allocation for meshed networks of mobile users in shared spectrum

Département Systèmes (LETI)

Laboratoire Sans fils Haut Débit

01-09-2021

SL-DRT-21-0186

mickael.maman@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

In future 5G wireless networks, it is imperative to easily deploy and manage a private network of mobile users such as a fleet of vehicles or UAVs. The objective of this thesis is to define a distributed resource allocation for mesh networks of mobile users in the shared spectrum through resource pooling (time/frequency) and efficient management of directional antenna beams. While existing studies focus on maximizing the performance of static backhaul multi-beam mesh networks, we are interested in collaborative local/distributed learning between mobile users. The first step of this thesis will be to integrate a realistic model of sub 6-GHz and/or mmW directional antennas in a network simulator. A trade off between the spatiality of the directivity, the antenna efficiency and the complexity of the algorithm will be made for point-to-point and point-to-multipoint communications. Moreover the antenna configurations will be contextualized between the communication, discovery or tracking phases. The second step of this thesis will concern the design of the distributed resource allocation protocol during different stages of the network life: deployment, self-optimization and self-healing. A trade-off will be made between the type and latency of antenna (re)configuration, the accuracy of beam alignment, the channel coherence time for mobile users (volatile connectivity) and the convergence time of the scheduling.

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Automatic design of secure hardware architectures

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Environnement de Conception et Architecture

01-10-2021

SL-DRT-21-0190

caaliph.andriamisaina@cea.fr

Cyber security : hardware and sofware (.pdf)

Embedded systems are more and more ubiquitous and interconnected; they are an attractive target for security attacks. The security aspect is thus becoming more and more important when designing these systems, as a vulnerability in one system can compromise an entire infrastructure of connected systems. Thus, each system contributes to the construction of a global chain of trust. Moreover, given the increasing complexity of the applications running on these systems, it is becoming increasingly difficult to meet all security criteria (for instance application isolation, system authentication, secret and private data protection, communications protection). The design of these systems therefore requires an in-depth analysis of the various security constraints to which they are subject, based on a threat model associated with the potential attacker. While extra-functional design objectives such as performance, power consumption and area are generally well taken into account during the very early stages of embedded system design, security is still generally considered afterwards, leading to security solutions seen as an addition to the initial system. This design approach needs to be reconsidered in order to develop solutions that integrate security by construction and no longer as an additional element. The objective of this thesis is thus to take into account the security constraints in addition to the performance, power consumption and area constraints during the design space exploration (DSE) of hardware architectures in order to automatically generate an architecture optimized with respect to all these constraints. This study will begin with an analysis of the threat models in particular with respect to hardware attacks and existing countermeasures at the hardware level. Then, the security modeling and quantifying in the context of DSE will be carried out, as it will be essential to clearly characterize the techniques and approaches for taking into account the security needs of the systems. From this step, the candidate will propose a DSE flow of hardware architectures taking into account security constraints, in addition to power consumption, performance and area constraints. The goal is to be able to analyze the security, area, power consumption and performance trade-offs according to the designers' specifications at both functional and non-functional levels. This flow will then be applied to a practical case of hardware architecture design in order to validate the developed DSE approach. The developed solutions will have to demonstrate their level of robustness with respect to the security constraint in order to guarantee the security of the systems while respecting and optimizing the other design constraints.

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Proof of functional equivalence of binary codes in the context of programs hardening

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Fonctions Innovantes pour circuits Mixtes

01-10-2021

SL-DRT-21-0192

damien.courousse@cea.fr

Cyber security : hardware and sofware (.pdf)

The general context of this thesis is the cyber-security of embedded systems. The research background of this thesis is tied to the automatic application of counter-measures against the so-called physical attacks, which encompass observation attacks (side-channel attacks) and perturbation attacks (fault-injection attacks). The CEA List is working on COGITO, a compiler toolchain based on LLVM for the automatic application of software counter-measures against physical attacks. Given a source-level implementation of an unprotected program, our toolchain produces an optimised binary program including targeted counter-measures, such that the compiled program is hardened against a specified threat model. Two key points are today crucial to trust the compiled programs: 1. the proof of robustness of programs produced by our toolchain, 2. the proof that adding counter-measures does not alter the functionality of the target programs. This thesis will target the second point: bringing formal guarantees about the functional correctness of the secured programs. We will use sound and exhaustive symbolic reasoning, supported by BINSEC (). BINSEC is an open-source toolset developed at CEA List to help improve software security at the binary level. It relies on cutting-edge research in binary code analysis, at the intersection of formal methods, program analysis, security and software engineering. The PhD thesis will be hosted at the CEA in Grenoble, in a multidisciplinary environment including experts in embedded software, cyber-security, hardware design, and machine learning. Short-term stays at the DILS at the CEA in Saclay will be planned throughout the three years of the thesis to collaborate with experts and developers of BINSEC.

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Innovative flexible piezoelectric sensors for tactile and acoustic interface ? Multifunction sensors

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs

01-09-2021

SL-DRT-21-0205

elise.saoutieff@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

The aim of the PhD thesis is to implement a matrix of flexible piezoelectric nanosensors, which enable the 3D reconstruction of a force or deformation field. The nanosensors based on GaN nanowires obtained by directed growth are fabricated and assembled at CEA. The candidate will tackle experimental aspects, which include the fabrication and the assembly of sensors and sensor networks (matrix) via controlled growth and deposition processes, first-level flexible electronic layers (interconnects), system integration on an object (mechatronics) and finally signal collection and processing through a dedicated reading electronics, to be designed based on the competences present in our laboratory. In parallel, the candidate will carry out studies at the fundamental level, such as investigating the mechanical transfer between the nanowire and its environment and its effect on the generated signal under deformation, or the study of the piezoelectric / pyroelectric coupling intrinsic to GaN nanowires. For this purpose, the candidate will have access to multi-physics simulation tools. Finally, investigations on the choice of materials and the characterisation thereof (structural, mechanical, thermal, optical, electrical) will be pertinent and may pursued. More generally, this PhD thesis will also provide the opportunity to develop applicable solutions in various fields such as deformation and impacts detectors for predictive maintenance, sensitive surfaces or electronic skin.

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Wideband mmW receiver architectures based on innovative modulation schemes

Département Systèmes (LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-03-2021

SL-DRT-21-0216

joseluis.gonzalezjimenez@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

Existing telecommunication and data communication networks are evolving towards extremely high capacity and data-rate connections that will require innovative transceiver architectures. For wireless data links, beyond 5G and 6G and systems will be required in the next 5 to 10 years able to provide 100Gb/s or higher data rates by efficiently using the wide spectrum available at millimeter-wave (mmW) or sub-THz frequencies. Traditional transceiver architecture that have been used in the past may result too power consuming or simply not performant enough to respond to this challenge. The LETI research institute has been conducting research during the lasts year in the field of innovative modulations schemes and transceiver architectures trying to respond to the above-mentioned high data-rate in wireless environments considering the limitation imposed by existing electronic devices required to build the transceivers. This thesis subject will explore the practical implementation of circuits based on innovative modulation schemes and architectures for high-speed, large-bandwidth, imperfection resilient mmW receivers for beyond 5G and 6G telecommunication applications and other high data-rate wireless communications applications.

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Single address space for massively parallel computers

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Systèmes-sur-puce et Technologies Avancées

01-09-2021

SL-DRT-21-0248

christian.fabre1@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

The generalization of a hierarchical organization of HPC machines into nodes of several dozen computing cores interconnected by a high-performance communication network has fragmented operating systems and greatly complicated the writing of applications. The proposal of a 128 bit processor architecture by the RISC-V community offers the possibility of reinterpreting the fundamental concepts in view of these fundamental changes in the structure of the machines. In particular, this proposal offers the opportunity to rethink memory addressing at the scale of the entire machine, and not locally at the level of each node. The purpose of this thesis will be to study the opportunities thus offered, to propose strategies for managing a 128-bit addressing space on the scale of the machine, and to evaluate its technical feasibility, hardware and software and expected performance. The Research Director for the PhD student will be Prof. Frédéric Pétrot, from Grenoble-INP/ENSIMAG. Christian Fabre (software) & Cesar Fuguet Tortorelo (hardware) from CEA LIST, will supervise the day-to-day work.

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Millimeter Wave Short Range RadCom

Département Systèmes (LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-09-2021

SL-DRT-21-0258

cedric.dehos@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

Mmw transceiver chips for high data rate, short range communication should be integrated soon in most mobile devices as contacless connectors. The wide bandwidth of these mmw transceiver may be used for fine resolution proximity radar, gesture recognition, biometric identification or Human Machine Interface. The thesis will investigate the possibility of slightly modifying the RF transceiver architecture to get the double feature of radar and communication in the same low cost and low power chip. The non-coherent communication architecture may evolve towards IR-UWB or FM-CW radar, with impact on the receiver complexity and radar performance. The PHD will evaluate the different alternatives, considering integrated low power radar processing and Artificial Intelligence embedded in MCU for analysis and classification.

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Formal verification of hardware micro-architectures to analyze the influence of faults injection and the robustness of countermeasures

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Environnement de Conception et Architecture

01-10-2021

SL-DRT-21-0276

Mathieu.Jan@cea.fr

Cyber security : hardware and sofware (.pdf)

The never race towards higher average performance for numerical systems leads to increase the complexity of modern hardware architectures. This threatens the design of secure embedded systems as it opens new attack vectors. For example, the Spectre family of attacks exemplifies the issues that speculative execution mechanisms can raise in modern processor architectures. In the context of this PhD proposal, the more complex a processor architecture is, the larger is the attack surface exploitable by fault injection attacks. Such attacks, performed by the means of a physical perturbation of the circuit, aim at exploiting a logical perturbation in the computing for various purposes: leaking secrets, bypassing authentication procedures, escalation of privileges, etc. The modeling of the logical effects of a physical perturbation over numerical systems has been studied extensively, but it is still challenging to model precisely. Furthermore, recent research work has shown that inducing faults in the processor microarchitecture can lead to subtle effects, opening interesting research questions about the modeling and the analysis of such effects, and possible mitigations. The objective of this PhD thesis is to investigate formal approaches on the hardware side to better understand the consequences of fault injections, as well as to verify the efficiency of countermeasures shipped in secure embedded systems.

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Sensor network and low-power Edge AI for predictive maintenance

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs

01-10-2021

SL-DRT-21-0312

vincent.heiries@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Predictive maintenance is a major issue for the industry of the future (Industry 4.0), allowing to maximize the use time of parts, increase the machines service lifes, reduce failures and equipment downtime, with economic and environmental gains for the company. Predictive maintenance relies on sensor networks placed on the equipment to be monitored and on learning mechanisms using artificial intelligence (Machine Learning). These sensors are today essentially wired, which makes their installation complex: cables installation, walls, rotating environments, ... The ideal solution would be to have wireless communicating sensors; then the question of their energy autonomy arises, which is the issue of this PhD thesis. This topic, which is part of the "Cyber-Physical Systems" roadmap of the Systems Department of CEA-LETI (Grenoble), will aim to develop a network of low-power wireless sensors to monitor industrial equipments and anticipate their failure. The PhD work will be based on advanced technological solutions using embedded artificial intelligence (edge AI), data fusion processing from different sensors (audio, vibration) and low-power electronics (hardware and firmware) in particular for the signals processing and communication aspects. Artificial intelligence is booming, with major challenges for health, transport, environmental protection and industry. At present, computations are mainly carried out on servers (commonly referred to as the cloud), which requires the complete transmission of data measured by sensors (e.g. an audio signal for a microphone, or vibrations for an accelerometer). This architecture is simple to deploy but not very energy efficient with oversized computing servers for the most part, and not very resilient in case of data transmission failure. The trend is therefore to implement processing algorithms as close as possible to the sensors in order to reduce the utilization rates of communication systems, offload the computational servers by reducing their energy consumption, and improve the resilience of these sensor networks. Based on this observation, it remains to be understood how a data processing task initially carried out by servers with no power and computing power constraints can be offloaded onto a local sensor network with limited available energy and reduced computing power (e.g. low-power microcontrollers). To this end, methods used in the field of compressive sensing and machine learning algorithms can be applied in the compressed space. The core of the thesis will thus focus on the minimization of hardware and firmware energy consumption of embedded electronic systems implementing artificial intelligence and aiming at the application "predictive maintenance for industry". Research questions and associated innovations will be targeted: (i) the development of low-power electronic architectures (wake-up functions, adjustment of measurement frequency, ...), (ii) the development and implementation on microcontrollers of Machine Learning algorithms for sensor functions (audio, vibration, temperature) and (iii) the development and implementation on microcontrollers of predictive Machine Learning algorithms for the optimization of energy and autonomy. A complete electronic device (hardware + firmware) implementing these innovations and deployed in a real environment is expected by the end of the thesis.

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SOI vertical diodes for LWIR detection

Département d'Optronique (LETI)

Laboratoire d'Imagerie thermique et THz

01-09-2021

SL-DRT-21-0313

patrick.leduc@cea.fr

Photonics, Imaging and displays (.pdf)

Uncooled thermal detectors absorb the infrared flux in wavelength range from 7µm to 14µm. This corresponds to an atmospheric transmission window and to the maximum emission of a blackbody at 300K, which enables to measure temperature variations of less than 100mK. The operating principle of microbolometers is based on the temperature measurement of a suspended membrane absorbing the infrared flux. The thermal transducer is the sensitive element of the microbolomètre, which determines its signal-to-noise ratio and therefore the performance of the bolometric pixel. Most commercial microbolometers use a thermistor with amorphous silicon or vanadium oxide for its high temperature coefficient (TCR = 2-4% / K) and low flicker noise (1/f noise). The thesis proposal deals with a breakthrough technology for microbolometers. Unlike conventional thermistor detectors, the student will examine the use of SOI vertical diodes as transductor. The topic will focus on characterizing and modeling the performance of such a device.

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Design and fabrication of GeSn based components for environmental detection

Département d'Optronique (LETI)

Laboratoire des Capteurs Optiques

01-10-2020

SL-DRT-21-0315

vincent.reboud@cea.fr

Photonics, Imaging and displays (.pdf)

One of the main challenges for silicon photonics is an integrated laser technologically compatible with microelectronic foundries. Traditional, standalone semiconductor lasers use III-V semiconductors that are not accepted in the silicon foundries, contrary to the group-IV semiconductors. CEA Grenoble is among only few labs that already demonstrated mid-infrared optically pumped lasing in group-IV semiconductors, both in Ge and GeSn. With fully relaxed or tensile-strained heterostructures and quantum wells made of silicon-germanium-tin alloys (Si)GeSn, today we target continuous lasing at room temperature and efficient photodetectors in 200 mm wafers. To reach room temerature lasing, we need to improve the optical gain and optimize the carrier confinement. The improvements will require a redesign of the quantum wells and heterojunctions in germanium tin, through playing with atomic compositions and the mechanical strain at the micro-nanoscopic scale. Like the lasers we already demonstrated, the designed (Si)GeSn layers will be epitaxially grown at the main 200 mm CMOS fab facility of CEA Leti, and further fab-processed by the PhD candidate in smaller-scale clean rooms. Laser developments will be used to realize efficient GeSn photodetectors. The objectives of the research will be: (i) to reduce the number of crystalline defects in the GeSn gain regions, (ii) to design efficient (Si)GeSn stacks that confine both electrons and holes, while providing strong optical gain (iii) to apply and control tensile strain in germanium tin layers, (iv) to evaluate the optical gain under optical pumping and electrical injection, at different strains and doping levels, (v) to design and fabricate laser cavities with strong optical confinement (vi) to obtain germanium-based group-IV lasers that are tunable and lase continuously. (vii) to test fabricated devices (light sources and photodetectors) in gas detection cells On a longer term, such lasers will be widely used in ubiquitous miniaturized, low-power devices for optical gas sensing and environmental monitoring. This work will imply contacts with foreign labs working on the same vibrant topic.

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Study and Integration of Monolithic GaN Cascode Transistor

Département Composants Silicium (LETI)

Laboratoire Composants Electroniques pour l'Energie

01-10-2021

SL-DRT-21-0326

julien.buckley@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

LETI is currently transferring an AlGaN/GaN power electronics technology on 200mm silicon wafers to a renowned industrial partner in the field of power electronics devices (silicon, SiC). Gallium nitride-based (GaN) power electronics transistors can have either a normally-off (e-mode) or normally-on (d-mode) operation mechanism. For security reasons, there is a preference for e-mode devices. There are three main types of methods to obtain such functionality: either by using a p-type GaN gate, a conduction channel that includes a MOS stack (metal, oxide, semiconductor) or a cascode architecture (by assembling an e-mode and a d-mode device). The cascode architecture for GaN-based devices is today highly successful as it can be driven with similar strategies than the ones used for more traditional silicon parts and because of its good reliability. The work of this PhD will consist in conducting a study aiming to optimize the design of the device and identify its key technological steps (epitaxy, deposition, lithography, implantation...) necessary to its fabrication, followed by the coordination of the tasks necessary to its processing in LETI clean rooms. An analysis and interpretation of the obtained electrical measurements will be performed by using finished element simulations (TCAD using Synopsys environment).

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Diagnostic and prognostic tools for inverters and PV modules using machine-learning approaches

Département des Technologies Solaires (LITEN)

Laboratoire des Systèmes PV Appliqués

01-09-2021

SL-DRT-21-0347

sylvain.lespinats@cea.fr

Solar energy for energy transition (.pdf)

Framework: In the current context of climate change, the issue of energy is central from a societal point of view and from a political or economic point of view. Solar production, which is a renewable alternative to carbon-based energies, is growing exponentially and this rise in power will probably continue in the years to come. One of the best ways to lower the financial and environmental cost of solar power plants is automatic diagnostics, which can detect and correct plant failures and thus increase their performance. Basically, photovoltaic power plants are made up of modules connected to an inverter. The modules produce direct current which is converted into alternating current by the inverter for transport on the distribution network. The failures and aging of these devices are the main source of non-trivial failures. For example, the lifetime of a power plant is generally estimated at 20 or 30 years, to be compared to only 10 years for an inverter. It is very common for the current and voltage upstream and downstream of the inverter to be monitored. These data are generally supplemented by meteorological measurements (irradiance and temperature in particular). These data are however under-exploited. In the case of the behavior of the modules, it is mostly due to the strong correlation with various factors including daily and seasonal phenomena, weather conditions, relative position of the sun, non-linear interactions between the different modules, aging continue, break, etc. In the case of inverters, the difficulties are mainly due to the strong dependence on the operating conditions and on the noise level of the measurement largely higher than the signal (as encountered in the context of the detection of gravitational waves by the LIGO project). Objective: From these data we want to provide a close monitoring of photovoltaic plants, diagnose failures and anticipate them. In that goal, based, on the one hand, on the very large amount of data which can counter the signal-to-noise problem, and on machine-learning on the other hand, we will isolate the different explanatory components. Firstly, the modules and inverters will be considered separately. Secondly, we will consider the system as a whole. In the past, the LSPV (CEA) and LAMA UMR 5127 (Savoie Mont Blanc University) laboratories have collaborated on the development of dimensionality reduction methods. These methods (probably to be adapted) make it possible to explore the datasets in order to extract behaviors that can be linked to various modes of operation and aging. This step will allow definoing classes for regression / classification methods. The final goal is a diagnostic tools deployable onto power plant monitoring systems. Desired profile: We are looking for a student in mathematics interested in applications in the field of renewable energy and electronics or a student in engineering sciences passionate about mathematics. Experience in electronics is not necessary, but the candidate may operate measurements in laboratory under the supervision of electronics and photovoltaic engineers to produce data or confirm behavior. The tools used may include dimension reduction methods, statistics (descriptions and tests), time series analysis, SVMs, neural networks or tensor methods.

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Scaling evaluation of ultra-low power BEOL integrated HfO2-based ferroelectric memory arrays towards 28nm node

Département Composants Silicium (LETI)

Laboratoire de Composants Mémoires

01-10-2021

SL-DRT-21-0362

laurent.grenouillet@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

The recent discovery of ferroelectricity in HfO2 thin films generates a strong interest in integrating this CMOS-compatible and scalable material in ultra-low power ferroelectric memories and neuromorphic circuits. Within the last 6 months excellent results were reported on HfO2-based ferroelectric capacitors integrated in the Back-End Of Line of 130nm CMOS, confirming their potential for non-volatile memory applications. In this context, HfO2-based ferroelectric capacitors will be fabricated and electrically characterized to assess their potential to be scaled towards 28nm node. The candidate will perform advanced electrical characterization on state-of-the-art TiN/doped HfO2/TiN single capacitors with different areas, as well as 16kbit FeRAM arrays. Remanent polarization, coercive field, and imprint values will be extracted as a function of cycling (endurance) and temperature (data retention) for various pulse voltages/durations. Based on array characterization where large statistics is available, the physical mechanisms responsible for the reliability issues will be investigated. The results will be used to optimize the capacitor electrical properties to further design low voltage, ultra fast, scaled FeRAM memory arrays integrated within 28nm FDSOI node.

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Advanced carrier recombination characterization for AlGaN/GaN HEMT, stack understanding from epitaxy to etching

Département des Plateformes Technologiques (LETI)

Laboratoire Analyses de Surfaces et Interfaces

01-09-2021

SL-DRT-21-0377

Lukasz.Borowik@cea.fr

Advanced nano characterization (.pdf)

To penetrate into the power electronics market, the main challenges for GaN remains the development of a reliable normally-off HEMT solution. CEA-LETI has chosen to develop recessed hybrid MISHEMT, fabricating normally-off devices which give functionality similar to a classic silicon based MOS. Since etching of AlGaN/GaN MISHEMT heterostructure can induce defects, it is critical to be able to characterize them in order to optimize gate processing and therefore overall device performance. Work will consist of: (1) CL and KPFM characterization realized and interpreted in term of radiative/non radiative carrier recombination for understanding of integration processes impact. Already optimized during previous internship sample preparation for correlated characterization will be refined for MISHEMT samples. (2) KPFM-CL correlation for a deep understanding of carrier dynamic in the gate stack (3) Correlation with electrical performances of theses devices will be performed. Instrumentation, besoins spécifiques: LETI owns all necessary equipment (CL and KPFM under illumination) for this thesis. Additionally, time-resolved CL is currently in PFNC investment road map and could be an interesting improvement during the third year of this PhD thesis.

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Near-field focusing techniques in inhomogenous media at millimiter wave

Département Systèmes (LETI)

Laboratoire Antennes, Propagation, Couplage Inductif

01-10-2021

SL-DRT-21-0378

antonio.clemente@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

In a huge number of applications such as wireless power transfer (WPT), microwave imaging, industrial control, etc., it is required to collimate, form or focus the electromagnetic radiation in a specific region of the space. Sometimes, this region could be located in the near-field region of the radiating element or surface. In this case, it is referred to as a near-field focused system. With the development of the future ?Beyond 5G? and 6G communication systems, the necessity to focus the radiating beam in the near-field region could be also required in the case of the reconfigurable intelligent surfaces (RIS). These kind of devices, when composed of reconfigurable elements, can be deployed to manipulate the electromagnetic waves and dynamically control and adjust the properties of the propagation channel. Eventually, near-field focusing could be also applied to future medical imaging systems at microwaves. These devices require focusing and collimating the electromagnetic energy in the human body tissues in order to diagnose, monitor and/or treat specific pathologies. In this context, near-field focusing can be used to improve the resolution of the imaging system by optimizing the energy transfer/transmission. The first objective of this thesis is to develop specific numerical tools for the synthesis, design and optimization of near-field focused systems in non-homogeneous media. These techniques will be developed by considering the electromagnetic properties of the media. The synthesis of the aperture field will be done considering modal expansion of the field and the potential vectors theory. After this phase, the synthesis and optimization procedures will be used to design a near-field focused antenna system operating at millimeter and/or sub-THz frequencies (30 - 300 GHz). These antennas will be manufactured and characterized in near-field test ranges. Measurements will validate the developed models for flat radiating apertures in specific scenarios. The possibility to perform measurements in a real applicative context (e.g. cancer detection) will be also considered.

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Lensless imaging and artificial intelligence for rapid diagnosis of infections

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Systèmes d'Imagerie pour le Vivant

01-10-2020

SL-DRT-21-0380

caroline.paulus@cea.fr

Artificial intelligence & Data intelligence (.pdf)

The objective of the thesis is to develop a portable technology for pathogen identification. Indeed, in a context of spread of medical deserts and resurgence of antibiotic-resistant infections, it is urgent to develop innovative techniques for rapid diagnosis of infections in isolated regions. Among optical techniques for pathogen identification, lens free imaging methods draws attention because they are the only ones currently able to offer simultaneous characterization of a large number of colonies, all with low-cost, portable and energy-efficient technology. The objective of the thesis is to explore the potential of lensless imaging combined with artificial intelligence algorithms to identify bacterial colonies present in a biological fluid. The thesis will aim to optimize the sizing of the imaging system (sources, sensors) and to study image processing and machine learning algorithms necessary for colony identification. Two cases of clinical applications will be studied.

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High efficiency power electronics transformer for renewable energy sources connected to the grid

Département des Technologies Solaires (LITEN)

Laboratoire Systèmes PV

01-09-2021

SL-DRT-21-0386

jeremy.martin@cea.fr

Energy efficiency for smart buildings, electrical mobility and industrial processes (.pdf)

The primary sources of electrical energy used in renewable energy systems are mainly with DC ouputs : We can indicate below, the main voltage characteristics of the power sources : -Photovoltaïcs (1.5 kVDC) -Energy storage systems (800V-1.5kVDC) -EHT Stacks (950 VDC) -Electric vehicle batteries (800VDC) On the other hand, the new energy transmission grids are also in DC : -HVDC: 100 kVDC to 1.6 MVDC Some rail power systems are also direct current: -Rail: 1.5 kVDC, 3 kVDC, SNCF 6 kVDC (experimental network project) DC collector architectures are foreseen in the following applications: -Distribution of energy in charging stations for electric vehicles -Onboard networks of naval propulsion machinery -Electric conversion chains for electric railway traction units -Production of photovoltaic energy -Stationary storage of electrical energy The objective of this thesis will be to obtain a modular DC / DC power electronics building block compatible with the voltage levels delivered by the ENR sources and allowing injection on the medium voltage DC. The electrical insulation of the primary sources will be unchanged: it will therefore be necessary to provide,the isolation of the sources through a very high efficiency transformer technology (> 99.5%) integrated at medium frequency into the conversion stages. The transformer will be one of the key elements of the problem and as such certainly the support for many innovations in terms of the use of magnetic materials (depending on the frequency range and the specification : amorphous materials, cut nanocrystalline, or specific ferrites can be used), the mechanical arrangement of these materials (orientation, charge rate, morphology), the electrical arrangement of the windings as well as the thermal management of the assembly, while ensuring an appropriate dielectric strength. -Injection can be done on a 6 kVDC network (SNCF experimental network) -The power electronics will be produced with high-voltage SiC semiconductors whose performance are far superior to Si équivalents semiconductors . The DTNM and the Ampère laboratory will provide their expertise on magnetic materials for the sizing of the transformer integrated in the conversion stages while the DTS will provide it's expertise in prototyping of medium/high power converters, prototyping of transformers , and also characterizations of power components.

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Multiphysical design of high-voltage power semiconductor modules for renewable energy conversion

Département des Technologies Solaires (LITEN)

Laboratoire Systèmes PV

01-09-2021

SL-DRT-21-0387

jeremy.martin@cea.fr

Solar energy for energy transition (.pdf)

Research and development around silicon carbide (SiC) power semiconductors provides samples that can withstand voltages up to 15kV. These devices switch at very high speeds (e.g. 120kV / µs for a 10kV SiC MOSFET or 180kV / µs for a 15kV SiC IGBT). Overall, the performances of these semiconductors are exceptional, and drastically reduces the switching losses compared to Silicon equivalents. The implementation of these switches is on the other hand very delicate and calls upon methodologies of multiphysics design in transversal disciplinary fields. It is, from the scientific literature addressed a number of scientific and technological obstacles that we can list: -Minimization of parasitic inductors of power modules (<5nH) -Integration of EMC shielding to collect disturbing impulse currents -Cooling of SiC chips so the size is very small compared to a Si equivalent -Management of partial discharges and dielectric materials -Influence of dV / dt on the aging of materials (in DC, at 50Hz, and in pulse) -Reflection phenomena (electromagnetic wave) The proposed work consists of studying and proposing a power module architecture integrating innovations making it possible to address the implementation of SiC chips up to 10kV. The teams from the CEA in Toulouse specialists in high power 3D packaging will provide their skills in assembly technologies for the production of complex power modules. The CEA teams at INES campus (Nat. Inst. of Solar Energy)located at the Bourget du Lac (Savoy) will provide their high voltage measurement and prototyping means as well as their knowledge in power module design (finite element simulation). Researchers from G2ELAB in Grenoble in cooling of power modules and dielectric science will use their knowledge as well as their experimental platforms.

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Oscillating neurons for computational optimization and associative memory

Département Composants Silicium (LETI)

Laboratoire d'Intégration des Composants pour la Logique

01-10-2021

SL-DRT-21-0393

louis.hutin@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Hopfield networks are a type of recurring neural network particularly well-suited to content-addressable associative memory functions. By giving its elements the ability to fluctuate at will, they can be adapted to efficiently solving NP-hard combinatorial optimization problems. Such problems, for which finding exact solutions in polynomial time is out of reach for deterministic Turing machines, find many applications in diverse fields such as logistic operations, circuit design, medical diagnosis, Smart Grid management etc. The frame of the proposed project is the search for hardware accelerators for Artificial Intelligence. In particular, we consider the use of injection-locked oscillators as neurons (ILO). The goals will be the design, fabrication and demonstration of such networks, featuring binary phase-encoded neurons coupled by adjustable synaptic weights, to carry out associative memory (e.g. pattern recognition) or combinatorial optimization tasks (ex: max cut, graph coloring,...).

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Synthesis and study of chiral organic materials for charge transport in organic semiconductors

Département d'Optronique (LETI)

Laboratoire des Composants Emissifs

01-10-2021

SL-DRT-21-0395

benoit.racine@cea.fr

Photonics, Imaging and displays (.pdf)

The detection and manipulation of the polarized light is very attractive, in particular because of the interest in using circularly polarized light (LCP) in many areas of societal importance such as technologies display, information transmission, cryptography, bio-medical imaging or even the detection of chiral molecules of pharmaceutical interest. Due to their ability to interact specifically with an LCP and to modulate its polarization, chiral molecular materials stand out as an element of choice for exploring these innovative applications and considering new potential in organic electronics. In addition, the specific property of chiral molecules to induce electronic spin selectivity in the conduction of electric current (CISS effect for Chiral Induced Spin Selectivity) also opens up opportunities in the field of organic spintronics. Consequently, the synthesis of innovative pi conjugated chiral semiconductors, presenting an easy modulation of their physicochemical properties and the integration of these materials in optoelectronic devices of the OLEDs, OPDs or OFETs type is of interest both fundamental and 'application. The thesis project will be done in collaboration with a CNRS chemistry laboratory in Rennes, France, and the LCEM laboratory (at CEA / LETI, Grenoble, France) specialized in organic semiconductors. The objectives of the thesis student will be to synthesize new chiral organic charge transporters and to characterize their photophysical (absorption and emission) and opto-electronic properties. The most promising molecules will be integrated into OLEDs and OPDs devices. The photophysical synthesis and characterization part (circular dichroism spectrometer, non-polarized and circularly polarized luminescence spectrometer, PER, etc.) will be carried out at the CNRS chemistry laboratory. The integration of molecules in OLEDs and OPDs devices will be done in the LCEM laboratory where the deposition equipment (PVD chamber for organic materials) and the opto-electronic characterization means (IVL, C (V), TLM, Photocurrent, hall effect, etc.).

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Deep Wireless Localization based on Artificial Intelligence for Challenging Environments

Département Systèmes (LETI)

Laboratoire Communication des Objets Intelligents

01-10-2021

SL-DRT-21-0398

benoit.denis@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

Various low-cost wireless localization technologies ans standards have emerged in recent years (e.g. UWB/IEEE802.15.4z standard, GPS RTK, cellular radio in millimeter wave bands...), fulfilling the needs of a variety of location-based applications and services (e.g., sustainable mobility and intelligent transport systems, smart cities, industry 4.0, cyber-security, etc.). However, despite the good theoretical performances of these systems, radio obstructions and multipath propagation degrade in practice both localization accuracy and continuity (e.g. vehicular navigation in urban canyons, indoor assets or pedestrians localization in dense industrial environments...). In the frame of these new PhD investigations, we propose to evaluate and demonstrate the promising potential of Artificial Intelligence, and more specifically, of advanced (deep) machine learning tools, to assess the richness and the complexity of received radio signals from a specific localization standpoint. Typically, one aim would be to benefit from the « hidden » location-dependent information contained in received mulitpath multi-link signals under user mobility. In constrast to conventional correction approaches based on a priori parametric models, which are most often too simplistic and hard to calibrate, we will thus consider learning and generalizing the fundamental non-linear relationships linking key location-dependent features (i.e., extracted out of high-dimension input received signals) and localization descriptors (e.g., relative/absolute position, speed, orientation, visibility conditions?). Accordingly, so-called « deep » localization strategies will then be developped, enabling the prediction, detection and/or completion of erroneous/missing location attributes, directly in terms of positioning and tracking at the system level (i.e., without requiring intermediary and independent link-wise correction steps). The designed learning architectures and localization approaches will be fed and validated by means of large radio databases, including both field measurements collected with real radio devices and synthetic data based on determinsitic prediction tools (e.g., ray-tracing).

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Lithography process for 3D high-resolution patterning

Département des Plateformes Technologiques (LETI)

Laboratoire

01-09-2021

SL-DRT-21-0409

jerome.reche@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Since 1960 the CEA-LETI (laboratory of electronics and information technology) is ones of main contributor of French innovation is electronics and new technologies. Its different entities are a real bridge between research and industry. One of them, the DPFT (technological platform department), includes the resources of manufacturing, the control and the associated environment, which allows to set up and to mature new processesto develop the future of electronics. Use of 3D structures to replace planar structures is one way to achieve this plan. Nevertheless, the current 3D structures, which are made and replicated, as microlens used in photonic field, use process scale for micrometer size and the new needs target sub-micronics size. The replication technology as high-resolution nanoimprint could adress this scaling challenge with yield improvement (time and cost reduction). The nanoimprint technology allows replication of 3D structure in one process step inside a functional material, this mean that the material imprint is the final use for application and not an intermediate one. The final aim of the thesis is the realization of 3D structures on a sub-micrometric scale (100 nm to 1 µm) and their replication. This involves firstly the creation of such structures using known but slow technology such as electron beam lithography associated with the transfer techniques in hard materials. Moreover, it will be necessary to characterize these structures at each stage in order to know their precise shape. In a second step, the candidate will be able to try to replicate these patterns with the laboratory nanoimprinting equipment and the various materials and processes already developed in the lab. The replicated structures will be finely characterize to evaluate morphological modification, defectivity obtained or possible non-uniformity. From these results, the candidate will have to implement a detailed analysis potentially associated with design of experiment (DOE) and the use of modeling to adapt the process and the starting structures to obtain the expected replication. The thesis contract will take place over 3 years with a gross monthly salary of 2 043.54? during the 1st and 2nd years, and 2 104.62? for the 3rd year. At the end, the skills developed by the PhD student enable him to work in many high technology sectors such as nano and microelectronics, materials chemistry or more generally data processing field.

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New physical layer on millimeter wave band for 5G-NR IoT

Département Systèmes (LETI)

Laboratoire Communication des Objets Intelligents

01-10-2021

SL-DRT-21-0410

valerian.mannoni@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

A new 5G air interface is to be designed, in order to address good reliability and acceptable latency service for IoT use cases not addressed yet by Cellular IoT technologies. This new 5G air interface is the subject of a study item in 3GPP release 17 and has been called NR_REDCAP (Reduced Capability NR devices). The ability to operate NR-Light on millimeter wave band is seen as required for Industry 4.0 applications and attractive for private networks due to its limited range and high spatial reuse. The objective of the PhD thesis is then to propose and investigate a new physical layer on millimeter wave band for 5G-NR IoT meeting the above challenges. The expected results are: - A better understanding of challenges and key enablers of 5G NR in millimeter wave band - Proposal of a new physical layer for 5G-NR IoT with the associated MIMO scheme - Proposal and study of the multiple access scheme based on MIMO - Identification and assessment of key NR-Light enablers in millimeter wave band to fulfill these requirements and reach the reduced complexity and cost target of NR-Light UEs while mitigating the performance degradation of such complexity reduction, for example coverage degradation.

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Superconductor Integration for thermal management of quantum computing and spatial multi-chip platforms

Département Composants Silicium (LETI)

Laboratoire Packaging et 3D

01-09-2021

SL-DRT-21-0411

jean.charbonnier@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

As part of Quantum Silicon Grenoble project, teams at CEA-LETI, CEA-IRIG and Néel Institute aim at building a quantum accelerator with silicon spin quantum bits (qubits). Compatible with large-scale production, existing integration processes on Si are a real advantage for the scalability of these qubits. The extreme qubit operating conditions (cryogenic temperatures =1K, high frequencies in the range of a few GHz, high signal density) require the development of adapted technological building blocks. To interconnect the qubits and the controlled circuits, integration of superconducting metals is promising. Indeed, their vanishing resistance at low temperatures and the low thermal conductivity of superconductors also enables to protect the qubits from the heat generated by the control electronics circuits integrated close-by. Note that these developments will also benefit spatial applications sharing similar operating constraints. The thesis will focus on: 1) Studying the superconducting properties of Nb, NbN, TiN, Al and any combination of these materials during integration processes to optimize them for a single level of routing and multilayers pads. Establishing low temperature compatible thermal conductivity measurement set-up, protocol and sample design. 2) Transferring the acquired knowledge in term of integration and thermal conductivities to develop the next generation of multi-chip platform hosting qubits and control electronics circuits. The PhD student will be part of the 3D integration and packaging lab of CEA-LETI (Grenoble) and will interact very closely with the Spectral-imaging laboratory for space science at CEA-IRFU (Saclay) for thermal conductivity measurements.

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Wearable dynamic fluidic chamber dedicated to transcutaneous monitoring of oxygen and carbon dioxide blood concentration

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Systèmes Pour la Personne

SL-DRT-21-0415

rodrigue.rousier@cea.fr

Health and environment technologies, medical devices (.pdf)

The development of wearable medical devices is a fundamental and essential in order to promote ambulatory medicine. So-called "conventional" as opposed to outpatient medicine commonly uses blood gas analysis to assess the efficiency of pulmonary exchanges and diagnose respiratory diseases. In particular, it detects an abnormal change in the oxygen and carbon dioxide concentrations in arterial blood going to the tissues. Since this analysis requires a blood test, it is therefore an invasive method and does not allow monitoring of concentrations in real time. An alternative to taking blood is a transcutaneous gas analysis, i.e. measuring the concentrations of gases that diffuse through the skin. This method is non-invasive and guarantees continuous monitoring of blood gases. The objective of this thesis is to study and develop an instrumented wearable dynamic fluidic chamber. This chamber will measure in real time the concentrations of oxygen and carbon dioxide which diffuse through the skin. The work will consist in modeling the gas exchanges between the skin and the fluidic chamber, then designing and instrumenting the chamber and finally testing it on a gas test bench. This subject requires a highly motivated person with skills in simulation, medical device design and instrumentation.

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Hybrid representation of polarising topics in political news

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Analyse Sémantique Textes et Images

01-02-2021

SL-DRT-21-0419

julien.tourille@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Existing recommendation algorithms tend to create filter bubbles which reduce the diversity of information proposed to users and lead to polarized opinions. This functioning mode is problematic particularly for domains such as politics, in which balanced and diversified information is needed to fuel a healthy debate. The main objectives of this PhD are to: (1) automatically detect topics which are prone to polarization, (2) propose new text representations which combine objective and subjective description criteria, (3) update the representations to take into account the highly dynamic nature of political texts and (4) propose new datasets and evaluation protocols which are adapted for the methods introduced in the paper. The results of the PhD work will constitute the entry point for diversified recommendation algorithms which aim to open the filter bubbles.

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Elastic wave sensors for field biological detection

Département Composants Silicium (LETI)

Laboratoire Composants Radiofréquences

01-09-2021

SL-DRT-21-0437

alexandre.reinhardt@cea.fr

Health and environment technologies, medical devices (.pdf)

Monitoring of the sanitary quality of water becomes increasingly a public health issue. In this context, CEA-LETI is developping sensors to detect bacteria in liquid samples. Among the technologies under investigation, electromechanical elastic wave sensors appear as particularly promissing. The aim of this PhD is to evaluate the use of such components, initially developped for radiofrequency signal processing, to biological detection in liquid samples. More precisely, the PhD subject proposed aims in a first stage at analysing the biological structures we want to detect, their possible interactions with a sensors and the associated detection mechanisms which could be exploited. This will allow the design of suitable sensors, by selecting the type of resonator, the vibration mode leading to the highest sensitivity and compatible with operation in liquids, and a scheme for the electronic readout of the sensor output. The candidate will then fabricate prototypes in the CEA-LETI clean rooms and will functionalize them and evaluate their performance in the laboratory. Ultimately, the sensors will be adapted so that they can be integrated in a multi-sensors detection platform developped in parallel by the CEA-LETI teams.

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Training and quantization of large-scale deep neural networks for transfer learning

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Artificielle Embarquée

SL-DRT-21-0446

johannes.thiele@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Training and quantization of large-scale deep neural networks for transfer learning Transfer learning is today a common technique in Deep Learning that uses the learned parameters of a generic network (a feature extractor) to accelerate the training of another network on a more specific task. This specialized network is subsequently optimized for the hardware constraints of the specific use-case. However, given that the representations of the feature extractor are often rather generic, it might be possible to optimize the parameters before the transfer, to avoid that each end-user has to perform this optimization by herself. In this context, the thesis has the following scientific objectives: - Using several ?unsupervised? learning methods (self-supervised, weakly supervised, semi-supervised) to train feature extractors on large datasets - Studying how common optimization methods (in particular quantization) can be applied on these extractors in a ?task-agnostic? fashion - Quantifying the influence of these optimizations on the transfer learning capacity, by benchmarking and theoretical analysis (e.g. information compression theory) Required competences: Master degree (or equivalent), machine learning (in particular Deep Learning), programming (Python, Pytorch, Tensorflow, C++), good English (French knowledge is not required, but helpful)

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Preserving temporal properties while dynamic reconfiguration of Cyber Physical Systems

Département Systèmes et Circuits Intégrés Numériques

Laboratoire pour la Confiance des sYstèmes de calcuL

01-09-2021

SL-DRT-21-0454

selma.azaiez@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

A 3-year PhD position is available in the List Institute of CEA (the French Alternative Energies and Atomic Energy Commission) to explore dynamic reconfigurations in Cyber Physical Systems with the constraint of preserving temporal properties to insure system safety. Cyber Physical systems are distributed and highly heterogeneous systems used in modern applications for different domains such as autonomous automotive, smart manufacturing, aeronautic, energy, etc. Such systems must obey to temporal properties such as latency or throughput that shall guarantee a correct behavior. CEA LIST teams are using formal models based on MoCCs (Models of Computation and Communication) to specify, analyze and validate such systems while applying them in a wide variety of fields. A static analysis would generate the initial configuration of the system respecting a given latency or throughput. Real-time monitoring would validate these properties during system execution. If deviations are detected, a dynamic reconfiguration of the system will be proposed that would utilize artificial intelligence algorithms as bases. This subject is important in various fields of applications: the autonomous automobile where the respect of the deadlines takes an important part in guaranteeing the safety of the system but also in fields such smart manufacturing where the production lines are brought to be easily reconfigurable while respecting throughput properties. The position is available at CEA Saclay Nano-innov at Palaiseau (near Paris), in collaboration with the DiverSE team at IRISA (Rennes). It will be supervised by Stéphane Louise (thesis advisor) and Selma Azaiez (co-supervisor). This position is dedicated to motivated students seeking for an ambitious topic and wishing to acquire experience in technological research related to industry. You must have an equivalent level of Master 2 with preferably a specialty in software engineering and / or operational research. Knowledge of formal methods as well as scheduling algorithms will help you achieve expected goals. Finally, you will have to present a good capacity for personal work, the ability to work in a team and a motivation for technical challenges.

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Situation awareness and task planning for a mobile manipulator in uncertain logistic environment

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire de Contrôle et Supervision Robotique

01-03-2021

SL-DRT-21-0459

eric.lucet@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

The proposed research project concerns the autonomous evolution of a mobile robot in a logistic context, for example an automated forklift truck. Based on an analysis of the situation, the system will have to be able to autonomously find the sequence of actions allowing it to move towards an object of interest, grasp it and place it in a predefined region. This must be done while avoiding collisions and possibly clearing the path to make possible the movement of the mobile base and gripper. In case of failure, the replanning inherent in the method must be able to find a new sequence of actions. It will thus be necessary to implement a statistical model of the current and future navigation situation in the proximity of a mobile robot equipped with a gripper system, as well as a task planning algorithm based on this model. The Situation Awareness model will be based on contextual data from the perception modules, the process and task models, the agents (robots and humans) present in the environment and their states, the intrinsic data of the robot and the geometric model of the environment. The detection of particular situations (anomalies, etc.) can be handled by data analysis and automatic learning algorithms, possibly with a training phase based on feedback and a priori knowledge. In particular, the Hierarchical Planning in the Now (HPN) approach integrates task and motion planning and deals with uncertainty. It avoids trying to find optimal solutions for the POMDP (which is insoluble), by constructing a deterministic approximation of the dynamics (model of the situation), establishing a sequential plan, and executing this plan while observing possible changes in relation to the expected results, and re-planning it when deviations occur. In addition, to deal with uncertainty about the current state, planning must be done within the belief space, which is the probability of distributions over the states of the world. Thus, based on preliminary work on this subject, an improvement of the hierarchical plan, as well as a better understanding and formulation of models of the modification of belief states resulting from these actions, will be investigated.

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Embedded autonomous incremental learning

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Intégrée Multi-capteurs

01-09-2021

SL-DRT-21-0465

carolynn.bernier@cea.fr

Artificial intelligence & Data intelligence (.pdf)

The recent development of incremental learning algorithms for deep neural networks is an opportunity to imagine new intelligent sensor applications deployed in real environments. By being incrementally able to learn new tasks, the sensor will be able to personalize its behavior to its specific deployment milieu, allowing it to adapt to slow variations of its targeted tasks (e.g. the detection of different types of anomalies) or learn new tasks that were not initially anticipated. This possibility would make the service rendered by the autonomous sensor more and more relevant. The objective of this thesis is the exploration of the means by which the intelligent sensor can become fully autonomous in its evolution while taking into account the limited processing capability of the embedded system. Also, seeing the limited power consumption of the platform, the idea is to associate two embedded systems, a first which is ?Always-on? and executes the nominal task of the application (e.g. the detection of different classes of events or anomalies), and a second which is ?On-demand? which would be executed now and then, in order to retrain the model of the ?Always-on? part. For coherence, it is necessary that the power consumption ratio of the two platforms be in a ratio of 1:100 to 1:1000 approximately. The challenges facing the design of such a system are many : The first is the design of detection mechanisms able to find false negative examples (slowly changing classes) as well as novel examples (new classes). These mechanisms must be executed on the ?Always-on? platform, with the associated implementation constraints. A second difficulty concerns the retraining phase which is executed on the ?On-demand? platform. This phase must take into account the structure of the ?Always-on? model in order to be able to retrain it with new examples. This both in order to slowly learn the modifications of the existing detection task or to learn a new task without forgetting the old ones. Since this a new application space, the PhD candidate must be able to have a wide understanding of the subject and will necessarily have to address a wide number of domains including different incremental leaning algorithms, different deep learning training algorithms, and the hardware requirements necessary for running these algorithms in the embedded context.

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Interconnection of ultra-thin electronic components in stretchable flex for medical application

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire Composants Organiques

01-10-2021

SL-DRT-21-0466

julia.degirolamo1@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Developments in flexible systems integrating ultra-thin silicon components and sensors offer new perspectives of application to the medical world. Indeed, it is conceivable to functionalize wearable patches, which allow the monitoring of vital and/ or physiological parameters such as heart rate, blood pressure, oxygenation of blood, body temperature etc... An important issue is the robustness of these systems under mechanical constraint. Depending on the location on the human body, the patch will have to endure more or less important stretches. The electrical interconnections in the flex with the silicon components, which are not stretchable will then be strongly stressed. The CEA-LETI has recently developed a generic wafer manufacturing process for flexible labels including thinned silicon components below 50 µm. However, the chosen flexible material was not stretchable. Within the framework of this thesis, new stretchable materials, robust and compatible with a wafer level integration will have to be identified. One of the objectives will also be the development of stretchable interconnection based on a biocompatible elastomer. This interconnection must be locally conductive and sufficiently adherent to support the patch manufacturing process and the maintenance of the chips throughout its use. The PhD student will take into account the developments of the CEA-Liten on an interconnection inspired by the structure of the legs of the gecko. Finally, with a view to reusing the added high-value components and improving the recyclability of the patch, this interconnection must also be repositionable and reusable.

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Hydrogenation of Liquid Organic Hydrogen Carrier by electrochemical reduction

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire des technologies de valorisation des procédés et des matériaux pour les EnR

01-10-2021

SL-DRT-21-0471

vincent.faucheux@cea.fr

Advanced hydrogen and fuel-cells solutions for energy transition (.pdf)

Hydrogen is expected to be the energy carrier of tomorrow due to the versatility of its ways of production and use. Nevertheless, its storage remains a major technological and scientific challenge. An alternative to the compression or liquefaction of H2 - energy-intensive and expensive processes - consists in storing and transporting hydrogen at atmospheric pressure and at ambient temperature (via existing infrastructures) using Liquid Organic Hydrogen Carrier (LOHC). These molecules can undergo reversible hydrogenation / dehydrogenation cycles in the presence of a catalyst. This technology therefore makes it possible to transport hydrogen from its production site (via electrolysis) to its site of use thanks to the these liquid molecules. A hindrance to the commercial deployment of this technology is in the energy efficiency of the whole process and the cost of the hydrogenation / dehydrogenation reactors. Indeed, hydrogenation / dehydrogenation reactions are highly exothermic / endothermic and require relatively high temperatures and efficient catalysts, often based on platinum group metal (PGM). In addition, the hydrogenation step requires the prior generation of H2 by electrolysis. The implementation of a direct hydrogenation of LOHC molecules at room temperature and pressure by electroreduction, would minimize the energy needs associated with this hydrogenation step, and would open the field of application of this LOHC technology.

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Development of advanced etch processes for new CMOS gate patterning

Département des Plateformes Technologiques (LETI)

Laboratoire Gravure

01-10-2021

SL-DRT-21-0472

aurelien.sarrazin@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

With quantum technology emergence, device architectures such as active and gate transistor are deeply modified. These nested structures impose a very high etch control to meet new device requirements. New challenges should be overcome to keep sub-20 nm pattern dimensions and also to increase material etch selectivity . To do so, we propose to develop Atomic Layer Etching (ALE) approaches. Thanks to these techniques, we should be able to multiply etch selectivity and to promote pattern shape fidelity by passivation and etch behavior dissociation. Characterization and mechanism understanding will help study evolution. Thus, we could be supported by numerous ways of characterization proposed by LETI's plaforms and a last generation plasma reactor. These studies take part of quantum development proposed by LETI structures for the next decade. Challenges, human and material support allow to propose comfortable conditions to lead these PhD program and to investigate result valorisation.

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Development of biofunctionalized photonic circuits for the analysis of volatile organic compounds at very low concentrations for environmental and medical applications

Département d'Optronique (LETI)

Laboratoire des Capteurs Optiques

01-10-2021

SL-DRT-21-0473

loic.laplatine@cea.fr

Health and environment technologies, medical devices (.pdf)

The identification and quantification of volatile organic compounds (VOCs) in the air is a crucial issue in many fields. The analysis of outdoor air makes it possible, for example, to monitor and control pollution linked to industry or motorway traffic. Likewise, the analysis of exhaled air allows the diagnosis of certain pathologies. This requires the ability to measure dozens of different VOCs at very low concentrations (ppb) in complex gas matrices. CEA Grenoble recently developed sensors using integrated silicon photonic circuits chemically functionalized by biomolecules capable of specifically capturing certain VOCs, similar to human olfaction. They are currently used for measuring odors. These biomimetic photonic sensors offer great potential in terms of miniaturization, improvement in sensitivity, multiplexing for the measurement of complex mixtures, and can be manufactured at low cost by methods derived from microelectronics. In particular, they make it possible to consider gas analyzes in situ and in real time. The thesis is positioned at the frontier of electronic noses and analytical systems and will aim at the design and instrumentation of an experimental device to improve the detection limit and identification. The thesis will be highly multidisciplinary and will include the design and characterization of integrated photonic circuits on wafers and chips, the design and characterization of microfluidic circuits, surface chemistry and biofunctionalization, as well as data analysis (classification, modeling, learning, etc?). We will explore certain applications at the end of the thesis, in particular on the analysis of exhaled air and the detection of atmospheric pollutants.

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Advanced control of inverters for stability of microgrid with high rate integration of renewable energy

Département des Technologies Solaires (LITEN)

Laboratoire d'Intégration Energétique Locale et Autoconsommation

01-09-2021

SL-DRT-21-0480

thai-phuong.do@cea.fr

Smart Energy grids (.pdf)

Energy transition policy, decreasing renewables energy production cost and continuous advancing of technology innovation help accelerating renewable production integration into power systems, especially in microgrids. Renewable production such as photovoltaic rate keeps increasing in such systems, targeting 100% renewable production microgrids. In this context, the study of microgrid stability without synchronous machine become essential. Among different axes of research and development, innovations relative to advanced control solutions in micrgrid and gird-forming converters is one of the most promising as that can help to address both frequency and voltage stability. Specifications on required control functionalities are still under discussion while research and industrial entities have started to release solutions with case-by-case application. Some functionalities remained to be developed/ validated in inverter control level. Indeed, large signals phenomena such as microgrid mode changing from grid-connected to islanded, or reversely, blackstart or short circuit fault are part of challenges to go through. The PhD objectives are to address the development of these functionalities, by: - Build of methodology for new microgrid stability with high rate or 100% of renewable production - Modelling and simulation of two microgrid case studies of different power scales, with test of state-of the-art control to address stability issues. Starting from state of the art controls and improve them for adaptive and robustness criteria - Experimental validation with HIL tests and participation to implementation of the proven control solution in inverter prototype

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Recycling of fluorinated polymers contained in new technologies for energy (NTE)

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire des technologies de valorisation des procédés et des matériaux pour les EnR

01-10-2021

SL-DRT-21-0502

emmanuel.billy@cea.fr

Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Fluoropolymers are today very widely used for their mechanical and chemical properties and their durability. Polymers are unavoidable in the field of NTEs such as proton exchange membrane fuel cells (Nafion membrane in PEMFCs), batteries (PVDF at electrodes), or photovoltaic panels (EVA at the glass cell interface). With the advent of carbon-free technologies, the issue of recycling has become central to bringing these technologies to market. Historically, recycling processes were designed for processing different technologies and large volumes. This has led to the establishment of pyrometallurgical processes (high temperature) that are robust, but destructive and non-selective. In a context constrained by strategic, legislative (recycling rate) and environmental issues, it is necessary to recycle "more" and "better". This thesis aims at finding new wet or dry ways for the treatment of fluorinated compounds. The use of ionic liquids for the solubilization of polymers will be a preferred route. Their intrinsic physicochemical properties (very low volatility and flammability) make them ideal candidates for overcoming safety and environmental issues. The thesis work will be divided into three parts. Firstly, a state of the art will be realized for the evaluation of conventional processes and media for the treatment of fluorinated compounds. The state of the art will be tightened on the fluorinated polymers used in the field of new technologies for energy (NTE). A second part will deal with the chemistry of polymers and solvents in which a polymer can be dissolved. A third part of a fundamental nature will aim at linking the macroscopic results to the structural evolutions of the polymers.

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New materials to decrease CO2 capture energetic cost

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire Micro-Sources d'Energie

01-10-2021

SL-DRT-21-0503

arthur.roussey@cea.fr

Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Reducing CO2 emission is the main challenge of our generation. The transition towards low carbon energy sources will take time and CO2 capture, either at emission sources or directly from the atmosphere, is a mitigation solution currently in fast development. CO2 capture is a cyclic process, with a first adsorption step who removes CO2 from a gas stream, followed by a thermal regeneration step, which yields highly concentrated CO2, which can be reused or stored. However, when directly captured from the atmosphere (~ 400 ppm), this process has a high cost ((100-400?/tCO2),[1],[2] due mainly to the energy needed during the regeneration step (~1500 kWh/tCO2). The PhD thesis candidate will synthesize new polyamines and study their interactions with CO2 and water with the objective of reducing the energetic cost of CO2 capture and to improe their thermal stability. [1] K. Z. House, A. C. Baclig, M. Ranjan, E. A. van Nierop, J. Wilcox, et H. J. Herzog, « Economic and energetic analysis of capturing CO2 from ambient air », Proc. Natl. Acad. Sci., vol. 108, no 51, p. 20428, déc. 2011. [2] M. Fasihi, O. Efimova, et C. Breyer, « Techno-economic assessment of CO2 direct air capture plants », J. Clean. Prod., vol. 224, p. 957-980, juill. 2019.

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Qualification and quantification of GaN, InGaN and AlGaN surface states :

Département d'Optronique (LETI)

Laboratoire des Composants Emissifs

01-09-2021

SL-DRT-21-0515

David.Vaufrey@cea.fr

Photonics, Imaging and displays (.pdf)

GaN-based µLEDs seem promising for augmented reality (AR) or virtual reality (VR) applications. Indeed, they would make it possible to produce screens with resolutions and luminances that had not yet been achieved. But these µLEDs suffer from a lower efficiency compared to their larger sibling. A commonly accepted explanation for this efficiency degradation lies in the existence of numerous surface defects induced by pixel singularization etching. The smaller the dimensions of the LED, the more important the surface defects play in the electro-optical behavior. Their presence, if they are shallow, can facilitate electrical injection, on the other hand if they are deep, they contribute to the degradation of the electro-optic performances of devices such as LED. This thesis aims to quantify and qualify the surface defects in GaN, InGaN and AlGaN that make up GaN-based µLEDs. The doctoral student will have to carry out by himself all the stages of realization of new components necessary for this study, starting with the design of the photolithography masks, the realization of all the technological steps and finally the electro-optical characterizations such as DLTS (Deep Level Transient Spectroscopy), DLOS (Deep Level Optical Spectroscopy) or photocurrent. At the end, the doctoral student will have to identify the surface defects that are the most limiting for the efficiency of LEDs and the most favorable to the injection of electrical carriers. The thesis will be carried out in close collaboration with Ph Ferrandis (thesis director) from Néel Institute (CNRS), N. Rochat (co-supervisor) from CEA Leti (PFNC Nano Characterization Platform) and David Vaufrey (supervisor) from CEA Leti (LCEM Emissive Device Laboratory). The thesis grant would be fully funded by the CEA Leti in Grenoble for a period of 3 years.

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Distributed silicon photomultipliers electronic readout for particle localization and identification from a pixelated scintillator

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-10-2021

SL-DRT-21-0517

gwenole.corre@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

The evolution of silicon photomultiplier technologies (SiPM) allows the implementation of pixelated measurements. The compactness of SiPMs opens up prospects for measurements distributed over larger and/or pixelated scintillator surfaces. The objective of the thesis is to provide a distributed electronic readout architecture in order to meet the application constraints of nuclear instrumentation. The work will start with a study of the state of the art of solutions to cope with the degradation of the signals from the detectors as well as the different methods for dealing with all application cases. This state of the art will define the constraints and the approach to integrate a distributed readout in the electronic board. In the second phase, the PhD student will realize a hardware architecture to manage a set of SiPMs to collect the light from pixelated scintillators. In order to assess the performance, various criteria will be studied such as spatial resolution, field homogeneity, detection efficiency and sensitivity, energy resolution, discrimination capacity. The developed system will be tested for one or more applications such as contamination mapping, radioactive source location and characterization, dosimetry mapping, particle beam characterization and calibration.

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Ammonia synthesis from N2 and H2O using an electrochemical-chemical lithium cycling

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire des technologies de valorisation des procédés et des matériaux pour les EnR

01-10-2021

SL-DRT-21-0522

Parviz.HAJIYEV@cea.fr

Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Hydrogen is a promising energy carrier if it is produced from renewable energy and stored / transported safely and at low cost. Ammonia has undeniable advantages for the storage of H2 with high energy densities (17.8% wt H2) and existing infrastructure for its distribution. Ammonia is produced on a large scale using the Haber-Bosch process under severe operating conditions (?450 ° C, ?200 atm). The hydrogen required for this process is produced from natural gas, emitting 3% of anthropogenic CO2. An alternative is to synthesize ammonia directly from renewable electricity using a lithium-based electrochemical-chemical cycle. This cycle involves different stages including the nitridation of Li (formation of Li3N), the hydrolysis of Li3N to generate ammonia, and finally the electrolysis of LiOH to regenerate Li and thus close the cycle. Setting up this cycle under moderate temperature / pressure conditions involves optimizing each steps in terms of kinetics and efficiency. Generating ammonia under these moderate conditions would greatly limit the ecological impact linked to this molecule.

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Predictive analysis, synthesis and validation of PGM free catalysts for a relevant decomposition of NH3 at lower temperature

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire des technologies de valorisation des procédés et des matériaux pour les EnR

01-10-2021

SL-DRT-21-0523

jerome.delmas@cea.fr

Advanced hydrogen and fuel-cells solutions for energy transition (.pdf)

Hydrogen is expected to be the energy carrier of tomorrow due to the versatility of its ways of production and use. However, conventional storage solutions (under pressure, liquid H2,...) have some drawbacks (cost, energy requirement, losses by diffusion or boiling). In this context, different alternatives exist like ammonia. Ammonia has undeniable advantages for the storage of H2 with high energy densities (108 kg H2/m3 NH3 at 20°C-8.6bar; 17.8% wt H2) and existing infrastructure for its distribution. Furthermore, its use either as NH3 or as H2 after decomposition makes it possible to consider ammonia for multiple applications. Its decomposition is endothermal and a high temperature (> 700 ° C) is mandatory to ensure its decomposition with high kinetics. This temperature implies the aging of the catalysts and has a strong impact on the mechanical strength of the reactors with time. Developing catalysts allowing the efficient decomposition (kinetics, cost) of NH3 at lower temperature, based on theoretical and experimental approach, would open the field of application of NH3 technologies.

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K-ion batteries, towards a full system without any critical raw materials

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Matériaux

01-09-2021

SL-DRT-21-0524

david.peralta@cea.fr

Electrochemical energy storage incl. batteries for energy transition (.pdf)

Classic Li-ion batteries are composed of a graphite anode and a cathode containing a lithiated layered oxide (formula LiNixMnyCozO2). The development and the generalization of the electric automobile market will generate stress on certain chemical elements source, especially for lithium, nickel and cobalt. In addition, the production method consumes a lot of energy (multiple calcinations) and several solvents/products used are not respectful of the environment (NMP, ammonia). The thesis aims to develop a battery technology based on potassium. We will pay attention not to use any critical element in order to significantly reduce the ecological footprint. In terms of performance, potassium has a potential close to lithium, which suggests that high-energy batteries can be manufactured. Some potassium cathode materials have theoretical capacities of 155 mAh/g at a potential close to 4 V, which makes the technology competitive with conventional Li-ion batteries. The final target of the PhD thesis is to optimize and validate the technology in a complete system. The student will optimize the synthesis of the cathode material, the anode, the electrolyte in order to obtain an efficient system.

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Perovskite materials: influence of nanocrystallization processes on the performances of PK cells for tandem integration

Département des Technologies Solaires (LITEN)

Laboratoire des Cellules Tandem

01-10-2021

SL-DRT-21-0526

noella.lemaitre@cea.fr

Solar energy for energy transition (.pdf)

After several decades of intense development, silicon-based PV is a well-established and mature technology that now nears its practical efficiency limit. One strategy to overcome this limit is to use multi-junctions solar cells coupling a silicon subcell with a topcell based on a higher band gap absorber (1,6 - 1,7 eV vs 1.12 eV for silicon) exhibiting high efficiency. Lead halide perovskites (with ABX3 structure) can fulfill these requirements. Such materials can be integrated via solution processing at low temperature and yield theoretical efficiencies well beyond 30% when combined in a tandem device with a silicon subcell. To this end, controlling perovskite crystallization from precursors solution is obviously of prime importance. Also, the development of upscalable techniques to process the perovskite is one of the main practical challenge still to be met to ensure a practical deployment of the technology. The strategy currently developed within CEA relies on a gas-quenching of the precursors wet film to trigger perovskite crystallization. The main goal of this PhD will be to study in-depth the perovskite crystallization when processed in such conditions, to pave the way towards successful integration in tandem devices.

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In-memory computing for sporadic applications in extreme environment

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Fonctions Innovantes pour circuits Mixtes

01-10-2020

SL-DRT-21-0529

jean-frederic.christmann@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

In the context of Internet of Thins, applications are mainly sporadic and operating conditions such as the energy budget might be extreme. Power consumption reduction allows to improve the lifetime of embedded platforms or to integrate enhanced features. This PhD subject thus tackles the design of a memory component which would be able to compute complex calculations directly within the memory. This would highly reduce data exchanges with the processor and reduce the associated power consumption. The computing memory component will be designed using asynchronous logic, which provides a natural solution for IoT applications and brings strong physical implementation opportunities to further reduce the power consumption. Architecture development, validation though digital simulations, physical implementation and characterization of the component's performances are the main steps of the PhD contributions. Fabricating a chip which integrates the proposed ideas would allow to demonstrate the quality of the chosen approach in a realistic context.

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On-Chip moniToring of Organoids using an oPtoflUidiC systEm

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Chimie, Capteurs et Biomatériaux

01-10-2021

SL-DRT-21-0534

charlotte.parent@cea.fr

Health and environment technologies, medical devices (.pdf)

The thesis is positioned in the domain of organoids, which enable fundamental tissue mechanisms to be studied in-vitro. The project aims at developing a new microfluidic system with integrated simple, robust and compact optical reading, and allowing for in-situ monitoring of the secretome evolution during the cell culture. The chosen model system is the culture of islets of Langerhans responsible for the endocrine function of the pancreas and playing an important role in diabetes. To reach our objective, we propose to combine two approaches within a microfluidic chip: a chip with photonic-crystal sensors that are compatible with lens-less optical reading (LED + CMOS), and a microfluidics technology integrating the pneumatic actuation of an elastic membrane. The main challenges of the project are related to the sensors sensitivity, the optical chip integration in the microfluidic system and the sampling of aliquots without perturbation of the organoid. The thesis will be in collaboration between two complementary laboratory, CEA-LETI (microfluidics, technology), and INL (photonic biosensors).

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Real-world data and AI for an innovative analytical approach to intonation and speech intelligibility in children with Cerebal Palsy

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire d'Interfaces Sensorielles & Ambiantes

01-10-2021

SL-DRT-21-0539

margarita.anastassova@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Cerebral palsy (CP) is a developmental motor disorder affecting an individual's ability to move, and maintain balance and posture. It affects 2 to 4 children in 1,000, making this lifelong, chronic condition the most common motor disability in childhood. In addition to motor problems, many children with CP have difficulties with speaking, which severely impacts their access to social and educational activities. The most frequent speech impairment in CP is developmental dysarthria, characterized by limited movement of the jaw, lips and tongue; imprecise articulation; slow speaking rate; reduced intonation with a limited variation in pitch, rhythm and volume, which results in impaired speech intelligibility. Despite the frequent occurrence of problems in intonation and speech intelligibility in children with CP, the heterogeneity of the profiles and the central role of intonation in communication, few studies have examined intonation patterns in this population in order to characterize and classify them. As a result, little is known about intonational difficulties and their relationship to intelligibility in children with CP. Knowledge on the relation of intonational patterns and expressions with motor activities in real-world tasks is even more scarce. The aim of the research project is to fill in the above-mentioned knowledge gaps. This will be done using a real-world data-driven approach, combined with innovative analytical approaches based on AI and machine learning.

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Fuzzing techniques to support static security analyses

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire pour la Sûreté du Logiciel

01-10-2021

SL-DRT-21-0557

michael.marcozzi@cea.fr

Cyber security : hardware and sofware (.pdf)

Guaranteeing digital security is a crucial issue. In this context, static analyses and fuzzing are popular approaches making it possible to detect software vulnerabilities directly at the binary level. The two approaches are complementary: static analyses can either provide guarantees that the program is safe, or report possibly unsafe program executions. On the contrary, fuzzing cannot provide any strict guarantee about program security, but is able to identify executions that exhibit a vulnerability for sure. In this thesis, we will thus take advantage of fuzzing to confirm vulnerability reports from static analysis. This will require us to develop a fuzzer able to target the possibly insecure executions identified by the static analyser. This is quite challenging as targeted fuzzing is a difficult and underresearched domain. In addition, static analysers provide rather loose descriptions of the possibly vulnerable executions, making the fuzzer's task even harder. To overcome these challenges, we will take advantage of tried and tested solutions from the fields of search-based testing and advanced coverage criteria, which have been underexploited in the field of fuzzing. The developed fuzzer will be evaluated at scale in real use cases and we will measure how much it enables a better prioritisation and a more precise elaboration of vulnerability reports. Finally, we will provide elements of generalisation, making targeted fuzzing more adaptable to problems sharing similarities with confirming vulnerability reports.

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Speculating About Low-level Security

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire pour la Sûreté du Logiciel

SL-DRT-21-0559

sebastien.bardin@cea.fr

Cyber security : hardware and sofware (.pdf)

We consider the general context of automated code-level security analysis. While standard attacks such as control-flow hijacking take advantage of programming flaws (typically, missing bound checks), recent micro-architectural attacks take advantage of subtle behaviours at the micro-architectural levels, typically speculative behaviours introduced in modern architectures for efficiency, in order to bypass protections and leak sensitive data. These vulnerabilities are extremely hard to find by a human expert, as they require to reason at a very low-level, on an exponential number of otherwise-hidden speculative behaviours, and on complex security properties (leaks and data interference, rather than standard memory corruptions). The goal of this doctoral work is to understand how automated symbolic verification and bug finding methods (especially but not limited to, symbolic execution) can be efficiently lifted to the case of speculative micro-architectural attacks, with the ultimate goal of securing essential security primitives in cryptographic libraries and OS kernels. This general objective raises challenges in terms of semantics of speculative behaviours, semantics of security properties and scalability of verification techniques. These techniques will be implemented in the binary-level code analysis framework BINSEC and their benefits assessed through rigorous experimental evaluation.

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Transfer Learning and Optimal Transport applied to the adaptation of models learnt on synthetic data

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Science des Données et de la Décision

01-10-2021

SL-DRT-21-0563

fred-maurice.ngole-mboula@cea.fr

Artificial intelligence & Data intelligence (.pdf)

This PhD thesis aims at exploring possible contributions of optimal transportation field to transfer learning through the following directions: - building a knowledge transferability criterion between a source and a target task based on the regularity of the transportation plan between the source and the target data distributions; - integrating priors on the tasks similarity through the transportation ground metric; - applying Wasserstein barycenter to multi-task learning problems. These works might find multiple use-cases of interest in the lab, including adaptation of models learnt on synthetic data to real world systems. A more detailed presentation of this PhD thesis subject can be found via the following link: https://drive.google.com/file/d/13RAQEi0PdnkllM-MHxQS50WWUNUtGS07/view?usp=sharing

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High Energy Density Positive Electrode based on Glass Materials for Li-Ion and Na-Ion Cells

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Matériaux

01-10-2021

SL-DRT-21-0577

sebastien.martinet@cea.fr

Electrochemical energy storage incl. batteries for energy transition (.pdf)

This PhD subject will aim at developing new positive electrode materials based on glasses for high Energy Density Li-Ion and Na-Ion cells. These developments will be held jointly between the laboratory of materials for batteries from CEA-Grenoble and LDMC lab from CEA-Marcoule that is specialized in the formulation and characterization of glass materials. The work will be focused on the optimization of the complex formulation of the glass cathodes to solve the issues related to first cycle irreversible loss and low cycling performances. The main objective will be to obtain one composition without critical raw materials exhibiting more than 1000Wh/kg at active material level vs 700 for state-of-the-art materials. This target will be reached with the support of advanced characterization techniques such as X-Ray Diffraction and RAMAN and FTIR spectroscopies. A dedicated effort will concern the development of operando or in-situ measures to be able to explain the link between electrochemical performances and glass characteristics, what has never been reported in the litterature.

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Polarization sensitive pixels

Département d'Optronique (LETI)

Laboratoire d'Imagerie sur Silicium

01-10-2021

SL-DRT-21-0588

quentin.abadie@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

For certain mass productions markets (automotive, industrial control, depth mapping etc?), polarization pixels could be useful. PhD will address this domain and weight pros and cons of current solutions. Differentiating from current competitors polarization sensors with Metal grids, the thesis project aims to explore new design/process to improve performances.

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Shell finite elements for real time simulations in virtual reality

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire de Simulation Interactive

01-09-2021

SL-DRT-21-0594

anders.thorin@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

With growing interest in interactive simulation comes the need to simulate shells (i.e. volumes with a thickness smaller than the two other dimensions) in real time. The objective of this thesis is to simulate the dynamics of thin shells in small strains, in the context of large displacements (geometric non-linearity) in real time. The work will be carried out in three parts, i) provide criteria to determine what can be simulated in real time or not (with a given number of degrees of freedom and a given precision), ii) identify or develop finite elements that are both robust to handle different use cases and efficient in terms of computational cost, iii) provide an implementation and examples of simulations that cannot be simulated in real time to date, with equal hardware.

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Development of superconducting single photon detectors and receiver circuits for quantum communications

Département d'Optronique (LETI)

Laboratoire d'Intégration Photonique sur Silicium

01-10-2021

SL-DRT-21-0605

segolene.olivier@cea.fr

Photonics, Imaging and displays (.pdf)

Quantum information processing turns out to be a major challenge for our society with the development of quantum computers, able to solve complex problems much more rapidly than a classical computer, and of quantum communications providing absolute security for information transfer. The development of integrated technologies is essential for the future large-scale deployment of compact and low-cost quantum information systems. CEA-Leti has been developing for several years a silicon photonics platform, providing integrated components and circuits for various applications such as telecom/datacom, lidars and more recently quantum communications. The objective of this PhD is in a first step to design, fabricate in the Leti clean room and characterize a new generation of advanced superconducting quantum detectors on Silicon able to detect single photons with above 90% efficiency. In a second step, these detectors will be integrated into secure quantum communication circuits. This PhD will benefit from collaborations with academic laboratories in France and in Europe.

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A new energy scale for X- and gamma-rays below 100 keV using high-resolution metallic magnetic calorimeters

Département Métrologie Instrumentation et Information (LIST)

Laboratoire de Métrologie de l'Activité

01-09-2021

SL-DRT-21-0608

matias.rodrigues@cea.fr

Advanced nano characterization (.pdf)

Currently, many physics experiments are probing exotic atoms (hydrogenic, pionic or kaonic) by X-ray spectrometry to study the structure of the nucleus and the nucleus-particle interaction or to test quantum chromodynamics (QCD) under extreme conditions. They are looking at electromagnetic transitions using very precise photon spectrometry. Other experiments aim at precisely measuring the weakest known isomeric transition, emitted by Th-229m, a candidate transition for the development of future nuclear clocks. New cryogenic detectors are being increasingly developed and used for these areas of research, but they require extremely precise energy calibration from reference lines. However, above 8 keV, the uncertainties of the evaluated and tabulated X and gamma lines increase sharply. The aim of the thesis is therefore to define a new scale in X and gamma-ray energy below 100 keV with a relative uncertainty of a few 10^-6. For this, two experimental set-ups will be developed and will integrate cryogenic detectors based on metallic magnetic calorimeters (MMC). These detectors, operating around 20 mK, offer an energy resolution one order of magnitude better than those of semiconductor detectors. An in-depth study of the whole measurement chain will have to identify and correct non-linearities and distortions. In addition, the MMCs will be pixelized to increase the counting rate and limit the statistical uncertainty. Once characterized and operational, the two cryogenic spectrometers will measure the X and gamma-ray energy spectra emitted by carefully selected radionuclides. The energies of the metrologically established lines will then be used as references, both for calibrating spectrometers used in fundamental physics and those used for materials analysis. Moreover, these energies will be a benchmark to validate complex theoretical calculations of radiative transitions.

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Blending intuition with reasoning - Deep learning augmented with algorithmic logic and abstraction

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des systèmes embarqués et autonomes

01-03-2021

SL-DRT-21-0617

shuai.li@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Within machine learning, deep learning, based on neural networks, is a subfield that has gained much traction since several high-profile success stories. Unlike classical computer reasoning, the statistical method by which a neural network solves a problem can be seen as a very primitive form of intuition, as opposite to classical computer reasoning. However, so far the only real success of deep learning has been its ability to self-tune its geometric logic that lets it transform data represented as points in n-dimension, to data represented as points in m-dimension, if we provide enough training data. Unlike a human being, a neural network does not have the ability to reason through algorithmic logic. Furthermore, although neural networks are tremendously powerful for a given task, since they have no ability to achieve global generalization, any deviation in the input data may give unpredicted results, which limits their reusability. Considering the significant cost associated with neural network development, integrating such systems is not always economically viable. It is therefore necessary to abstract, encapsulate, reuse and compose neural networks. Although lacking in deep learning, algorithmic logic and abstraction are today innate to classical software engineering, through programming primitives, software architecture paradigms, and mature methodological patterns like Model-Driven Engineering. Therefore, in this thesis, we propose to blend reusable algorithmic intelligence, providing the ability to reason, with reusable geometric intelligence, providing the ability of intuition. To achieve such an objective, we can explore some ideas like integrating programming control primitives in neural networks, applying software architecture paradigms in neural networks models, and assembling modular systems using libraries containing both algorithmic modules and geometric modules. The results of this thesis will be a stepping stone towards helping companies assemble AI systems for their specific problems, by limiting the costs in expertise, effort, time, and data necessary to integrate neural networks.

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Architecture and design of a room-temperature feedback loop between cryogenic Qubits measurement and control chains

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Systèmes-sur-puce et Technologies Avancées

01-09-2021

SL-DRT-21-0620

eric.guthmuller@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

A functional and useful Quantum Processing Unit (QPU) able to solve real problems on hundreds of Qubits will necessitate a way to correct errors introduced by quantum gates and Qubits decoherence. The foreseen way of doing this is to use error-correcting codes, such as surface codes for example. The common denominator to these error-correcting codes is the requirement to read regularly a subset of the Qubits and to apply operators to correct errors according to the measurement result. It is essential to reduce as much as possible the time between measurement and error correction, as in the meantime the Qubit error is still accumulating. The first objective of this PhD involves proposing an innovative low-latency (less than 1µs) digital architecture to correct errors in a real spin Qubits device. The second objective is to design this architecture on a FPGA board that is already used in Qubits measurement and control experiments. Finally, the student will do experiments on real Qubits devices placed in a cryostat to which the student will have access.

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Study of EBIC and cathodoluminescence applied to narrow gap photodiodes for cooled IR détection

Département d'Optronique (LETI)

Laboratoire d'Imagerie IR

01-10-2021

SL-DRT-21-0626

pierre.bleuet@cea.fr

Photonics, Imaging and displays (.pdf)

Since 40 years, CEA LETI is developing IR detection technologies using narrow gap semi-conductors. This led to the creation of the Lynred Company, formerly Known as Sofradir, now leader in the IR imaging market. In the frame of the collaborative work we have with Lynred for the development of new generations of IR imager, new characterisation needs appear. It addresses different issues, starting from the fine understanding of the photodiode operation when reducing its pixel pitch. It also addresses the understanding of the effects of the metallurgical and technological induced defects on the final IR detection performances. The proposal here is to study the behaviour of narrow gap IR photodiodes when excited with an electron beam within a scanning electron microscope (SEM). A mapping of the electron beam induced current (EBIC) brings important information about charge transport in the narrow gap, whereas the mapping of the associated induced IR luminescence (cathodo-luminescence) carries further and complementary information about radiative recombination of injected charges. This information is particularly interesting when interaction with defects occurs in the structure. In our characterisation group, the EBIC experiment is now operational at cryogenic temperatures. On the other hand, the cathodo-luminescence part of the experiment has to be developed to complete EBIC images. Once operational, the full picture EBIC+cathodo should be investigated using different samples from our fabrication line, focusing on small pixel pitches and high operation temperature structures, making the connection with all the other electro-optical characterisation benches available in our lab.

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Accounting for the Environmental Impact of Digital Systems during the Design Phase

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Systèmes-sur-puce et Technologies Avancées

01-09-2021

SL-DRT-21-0630

adrian.evans@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Currently, the green house gas emissions from digital systems are comparable to those from the aviation seector and unfortunately, they are following an exponential growth curve. This PhD thesis targets to realize a thorough analysis of the environmet impact due to the design phase et usage of large digital circuits. Following this analysis, the objective it to offer new tools and associated design methodologies to circuit designers, allowing to estimate et then reduce the environment impacts. During the design phase, it is targetted to reduce the amount of computing ressources, but the major gain will come from increasing the Energy efficiency and reduce the power enveloppe of digital system during its lifetime. This requires to analyse globally the system, from circuit level up to task delegation in the cloud. As a world leader in intelligent digital systems, the digital design departement targets to develop, through this PhD, a sober design flow that will be transfered to the industry. The PhD work will be using the overall design platform of the digital circuit and system departement, using different high performance computing systems as potential test-cases, and will define and optimize some cross layer optimization techniques.

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Dispersion-engineered metalenses for high-performance low-profile antennas and extreme wave manipulation

Département Systèmes (LETI)

Laboratoire Antennes, Propagation, Couplage Inductif

01-10-2021

SL-DRT-21-0638

francesco.fogliamanzillo@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

The performance of conventional antenna architectures, e.g. phased arrays and reflectors, is limited by inherent trade-offs among bandwidth, efficiency, scan range and size. Disruptive concepts are necessary to address the demanding requirements arising in novel applications such as satellite and 5G communications, and high-resolution imaging. Metasurfaces (MSs) are electrically thin arrays of subwavelength scatterers and have recently emerged as a promising concept to attain unprecedented antenna performance and functionalities. The subwavelength periodicity of a MS offers an extremely fine control on the aperture fields and, by virtue of Huygens' principle, the possibility to perform a wide range of transformations of the incident field. However, there is a lack of advanced models and synthesis methods for the design of anisotropic MSs. Moreover, the bandwidth of most MS antennas is often too narrow due to frequency dispersion. This thesis aims to provide a mathematical framework for the analysis and design of MS lenses and to demonstrate groundbreaking ultralow-profile antenna systems comprising a primary source and a metalens realized with a few cascaded layers. The metalens will be modeled as an effective bianisotropic medium, exhibiting coupled responses to electric and magnetic fields. Specific synthesis procedures will be developed to engineer the frequency dispersion of a large metalens and to tailor its refractive index as a function of the incident angle of the impinging wave. These tools will be exploited for the design of two demonstrators at microwave frequencies: (i) a high-gain antenna achieving extremely high aperture efficiency (>70%) and large fractional bandwidth (>15%); (ii) a thin meta-radome for extending the scan range of a phased array beyond the state of the art (±75°) while preserving high broadside efficiency. At least one demonstrator will be prototyped using low-cost fabrication processes, such as printed circuit board and additive manufacturing, and experimentally characterized.

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Scalable and Precise Static Analysis of Memory for Low-Level Languages

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire pour la Sûreté du Logiciel

01-10-2021

SL-DRT-21-0641

matthieu.lemerre@cea.fr

Artificial intelligence & Data intelligence (.pdf)

The goal of the thesis is to develop an automated static analysis (based on abstract interpretation) to verify, in large code base in low-level compiled languages (e.g. C, C++, assembly, Rust, Fortran), security properties that are related to memory, lke flow information properties and absence of memory corruption. This problem has many applications in cybersecurity, as most of the software-related cybersecurity issues, and those that have the highest severity, come from memory safety errors (e.g. (buffer overflows, use-after-free, null pointer dereferences, wrong type punning, wrong interfacing between several languages, etc). The three main issues when designing such an automated static analysis is to keep the verification effort low, to handle large and complex systems, and to be precise enough so that the analysis does not report a large amount of false alarms. The privileged approach in this thesis will draw on the success of a new method using abstract domains parameterized by type invariants, which found a sweet spot between precision (i.e. few false alarms), efficiency (in computing resources), and required effort (by the user). This method allowed in particular to fully automatically prove absence of privilege escalation and of memory corruptions of an existing industrial microkernel from its machine code, using only 58 lines of annotations. Many research questions remain, and we will explore how to extend the analyzer to improve scalability (using compositional analysis), how to improve its expressivity (to show complex security properties like non-interference), how to improve precision without degrading efficiency, or how to further reduce the amount of annotations (using automatic inference of more precise type invariants).

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Trajectory Prediction for Autonomous Navigation

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Intégrée Multi-capteurs

01-09-2021

SL-DRT-21-0644

tiana.rakotovao@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

With the growing interest in Autonomous Vehicles (AV), perception systems play a central role in their navigation, with active developments from the research and automotive industry communities. Perception systems provide AVs with information about the driving situation. Basically, advanced algorithms model the vehicle environment using a map by processing past and present data from on-board sensors such as cameras, LiDARs, radars and ultrasounds. The future evolution of the driving environment is predicted in order to plan safe trajectory, avoid collisions and make navigational decisions. CEA has developed a patented on-board sensor fusion technology that exploits the occupancy grid paradigm to model the vehicle environment. This grid provides a probabilistic estimate of occupied and free regions. The estimation of obstacle movement is also under development. However, a prediction layer that estimates the likely future trajectories of moving obstacles is still missing. The objective of the PhD thesis is to develop an embedded trajectory prediction algorithm for autonomous navigation. Trajectory prediction is a spatio-temporal (4D) problem where uncertainty is essential to evaluate the probable short-term evolution of a driving scenario. The diversity of moving obstacles makes trajectory prediction very difficult when integrated within lightweight computing platforms. In fact, a moving car does not have the same degree of freedom as a pedestrian. Prediction models can take into account the nature of moving obstacles if this information is available (for example, provided by artificial intelligence). Otherwise, prediction models must adapt to the available data. During the thesis, the PhD student will first focus on the probabilistic modeling of motion and trajectory. Then, he/she will propose a low-complexity algorithmic solution that can run in real time on an embedded computing platform. The PhD student will be hosted in a team whose expertise is the development of advanced and lightweight perception solutions that can be integrated into embedded systems. The PhD student will collaborate with researchers, engineers and other PhD students from various scientific fields. The candidate must have a strong mathematical background in probability/statistics, computer science and software prototyping (matlab/python, C++). Knowledge and skills in artificial intelligence and data fusion will be a plus.

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Dynamic Mesh Reordering by Cache-aware Heuristics for scientific computing

Département Systèmes et Circuits Intégrés Numériques

Laboratoire pour la Confiance des sYstèmes de calcuL

01-10-2021

SL-DRT-21-0653

thierry.goubier@cea.fr

Numerical simulation (.pdf)

In the context of simulation codes on unstructured grids, two elements appears: the traversal of the mesh on a CPU appear as random, and the "unstructured" aspect of the mesh makes accelerators problematic. However, such meshes allow the simulation at multiple scales, replacing multiple nested regular grids by a single structure, for example in tsunami simulations, resulting in a more compact structure and decreasing the computational power needed. Additionally, in some contexts the mesh will evolve in time, making partionning for multiple compute units difficult with their associated caches. This thesis focuses on the development of cache-aware heuristics of traversal of unstructured meshes, so as to allow on-the-fly reordering, in particular when sending compute kernels and mesh data on accelerators, so as to start computing before the mesh transfert has ended. This thesis will rely on existing results on mesh partitionning with tools such as SCOTCH and Metis, on models of memory / cache hierarchies and transfert capabilities (accelerators), and space filling curves related work (Hilbert-Peano, Sierpinski). The focus will be on the TsunAWI and FESOM (Finite Element/volume Sea-Ocean Model) codes from AWI and OpenFOAM. The student may have the opportunity to spend time at AWI (Germany) during the PhD.

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Real Time Semantic extraction on sparse data for embedded perception

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Artificielle Embarquée

01-10-2021

SL-DRT-21-0656

mehdi.darouich@cea.fr

Artificial intelligence & Data intelligence (.pdf)

The thesis topic we propose is in the field of embedded architectures for the semantic analysis of sparse data in real-time. During the last decade, the analysis of image and videostreams has experienced a boom following the significant improvement of neural networks and the increased specialization of the associated processing architectures. Research has led to the development of more efficient networks, which are less memory-intensive and increasingly integrable into embedded hardware. Several works are currently in progress in the laboratory, around the N2D2 tool for the optimization and integration of neural networks on embedded hardware, as well as around the DNeuro embedded hardware architecture. Within embedded perception systems, the strong constraints on bandwidth and memory lead to the privileged use of sparse data (graphs, point clouds, etc.), reduced in quantity and containing particularly rich information on the environment to be analyzed. However, the non-contiguous and unpredictable structure of this sparse data is very different from a traditional image stream, making current hardware architectures unsuitable for their execution. However, these particular characteristics point to very interesting opportunities in terms of optimization and efficiency. This thesis work aims at exploring this class of algorithms and their capacity of integration under constraints in an embedded computing architecture. The scientific problems that arise here are how to perform efficient data management in a context of highly scattered computational distribution, the compatibility of sparse data analysis algorithms with execution on embedded targets, and the performances and precision achievable under these constraints.

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Abnormal event detection in videos

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Vision et Apprentissage pour l'analyse de scènes

01-10-2021

SL-DRT-21-0659

aleksandr.setkov@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Abnormal event detection in videos by Deep Learning is essential in applications such as video surveillance, road safety, or autonomous driving. However, there are numerous technical and scientific obstacles such as scarcity and heterogeneity of such events, as well as scene complexity. Supervised methods require labelled data in large quantity which is difficult to obtain in this context. Anomalies are rare compared to normal data and video annotation is a tedious task. Another group of methods, called One-Class, uses normal data only to model normality in the data. However, this modeling is often hindered by the heterogeneity of both normal and anomaly data. The PhD work will focus on the methods that require a few examples (Few-Shot Learning) and/or semi- or weakly-supervised methods. The proposed new approaches will be evaluated on academic datasets as well as on real data of scenarios observed by static (video surveillance, industrial inspection, or detection of incidents) and dynamic (autonomous driving) cameras.

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Contribution of machine learning and probabilistic modeling of uncertainties for the design and model-predictic control of distributed multi-energy systems

Département Thermique Conversion et Hydrogène (LITEN)

Laboratoire des systèmes énergétiques pour les territoires

01-09-2021

SL-DRT-21-0664

mathieu.vallee@cea.fr

Smart Energy grids (.pdf)

Taking uncertainties into account is key for model predictive control of distributed multi-energy systems (DMES), as well as for sizing these systems when optimal control is involved. Solutions exist for dealing with uncertainties in optimization problems. However, fast computation is often required to allow for probabilistic methods, typically based on a Monte-Carlo approach. Designing models for fast computation introduces new uncertainties, related to the accuracy of the model compared to the real system under study. This source of uncertainty is hard to quantify, and can have huge impact on results if they outweight other sources of uncertainties. In this PhD work, we propose to design, based on previous work at CEA, a method for simplifying optimization models in a way that is both appropriate for dealing with uncertainties and for using then in control and sizing of DMES. The key novelty in this work will be on model uncertainty quantification and comparison with other sources on uncertainties, using innovative methods related to Bayesian Neural Networks and Deep Learning, in relation to more traditional physics-based modeling.

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ab initio simulation for X-ray spectroscopy

Département Composants Silicium (LETI)

Laboratoire de Simulation et Modélisation

01-10-2020

SL-DRT-21-0665

jing.li@cea.fr

Advanced nano characterization (.pdf)

Context: Photoemission Spectroscopy (PES) is the most powerful experimental technique to directly access the electronic structure of matter. X-ray Photoemission Spectroscopy (XPS) is common and useful to characterize the chemical composition of materials by detecting the energy level of core electron states. Recent XPS experiments performed at CEA-Leti show that the electron core levels in an alloy material with dopant are shifted systematically with the doping concentration and the alloy-mixing ratio. Such a fact suggests that XPS experiments may give more information on the local atomic structure other than the standard chemical composition. The XPS spectra can be simulated directly by ab initio methods, which helps to correlate the core-level shifts and local structures. Objectives and Tasks: This Ph.D. thesis is devoted to exploring the physical origin of the core-level shift by using a range from ab initio methods at different levels of theory, from density functional theory (DFT), many-body perturbation theories (GW), and quantum-chemistry approaches (coupled-cluster, configuration-interaction, and so on?). The second objective of the Ph.D. is to develop an efficient and reliable method to simulate XPS spectra, to interact with experimentalists internally in CEA-Leti. · Performing ab initio calculations. · Investigate and identify the physical origin of the core-level shift. · Propose and develop an efficient method to simulate XPS spectra. · Apply the developed method to the problems, the systems and experiments carried at CEA-Leti within a collaboration with experimentalists.

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Deep learning for multi-modal and multi-resolution electron tomography reconstructions

Département des Plateformes Technologiques (LETI)

Laboratoire Microscopie Mesures et Défectivité

01-09-2021

SL-DRT-21-0674

zineb.saghi@cea.fr

Advanced nano characterization (.pdf)

With recent advances in instrumentation and numerical methods for inverse problems, electron tomography is becoming a key 3D characterization tool capable of addressing current challenges of miniaturisation of microelectronic devices. With ultra-fast spectrometers for electron energy loss spectroscopy (EELS) and multi-detector systems in energy dispersive X-ray spectroscopy (EDX), it is now possible to acquire several signals simultaneously to reconstruct in 3D the structure and morphology of an object with sub-nanometric resolution, as well as its chemical composition with a resolution of a few nanometres. In the framework of an interdisciplinary project, we have implemented compressed sensing approaches for EELS/EDX tomographic reconstruction from a very limited number of projections. The quality and resolution of the chemical reconstructions were greatly improved, but the volumes were reconstructed separately. The objective of this PhD thesis is to develop a deep learning based methodology to take advantage of the multi-modal and multi-resolution aspect of EELS/EDX tomography. This approach would allow: 1) A gain in execution time and signal-to-noise ratio, 2) Simultaneous reconstruction of the volumes from all signals, 3) An improvement in the resolution of chemical volumes by taking into account morphological information.

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Fast detection of AMR bacteria in water through mid-IR imaging and isotope probing

Département d'Optronique (LETI)

Laboratoire des Capteurs Optiques

01-10-2021

SL-DRT-21-0678

mathieu.dupoy@cea.fr

Health and environment technologies, medical devices (.pdf)

Currently the challenge is to develop automatic and non-invasive measurements to enhance early identification or diagnosis. The optical technologies are the label free methods to detect and identify the chemical composition of the sample. Infrared spectroscopy is a widespread and reliable method to obtain a spectral fingerprint of the sample based on absorption of Mid-IR light. An optical platform has been developed to measure the absorption of light through the sample, combining quantum cascade laser (QCL) and bolometer matrix. The objective of the thesis is to explore the potential of Mid-IR imaging and isotope probing to assess the bacteria antimicrobial resistance. The thesis will aim to create the biological protocol mixing the isotope probe and the constraints of infrared radiation, to carry out the images on several bacteria species, to implement the data processing algorithms in order to evaluate the relevance of this approach.

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Experimental and numerical study of heat storage with phase change material with two heat transfer circuits

Département Thermique Conversion et Hydrogène (LITEN)

Laboratoire des composants et systèmes thermiques

01-10-2021

SL-DRT-21-0684

fabrice.bentivoglio@cea.fr

Energy efficiency for smart buildings, electrical mobility and industrial processes (.pdf)

Thermal storage is a key technological component to decorrelate heat production from its use. For steam, phase change material (PCM) storage technology is particularly relevant for many applications and is benefiting from many researches. However, to make the low carbon energy transition effective, two new needs are emerging, in particular for the production of hydrogen by high temperature electrolysis (EHT) and for "Carnot batteries": use two different fluids for the supply of energy and its restitution; simultaneously load and unload storage. The aim of the thesis is to experimentally study a MCP storage with two heat transfer fluids at a laboratory size. The tests aim to understand the dynamic and thermical behavior of this type of storage, made much more complex due to two heat transfer circuits. In addition, the management of such a storage will have to be redefined compare to the strategies used for a single coolant MCP storage. A modeling part will complete the experimental one in order to define of macroscopic modeling of the storage based on the experimental results.

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Gamification for empowering engineers collectives

Département Ingénierie Logiciels et Systèmes (LIST)

Lab.systèmes d'information de confiance, intelligents et auto-organisants

01-09-2021

SL-DRT-21-0692

sara.tucci@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Technologies, and in particular digital technologies, are part of the range of solutions proposed to address the many societal and environmental challenges, such as the 17 sustainable development goals listed by the United Nations. Faced with the complexity of the systems to be implemented, collective intelligence is a major key to success. Multidisciplinary and holistic, systems engineering practices as formalized by INCOSE, and more particularly in their model engineering based version, are similar to collective intelligence practices, and therefore its performance relies on the group's ability to communicate and therefore on the collaborative work tools shared by the team. However, users often consider the tools as complex and the resulting "misuse" becomes a hindrance to the performance of the collective instead of a stimulus. This thesis aims to explore the fields of serious games and game theory in order to reverse this trend and make software tools allies of development rather than enemies to be fought.

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Optical optimization of reception components in a 3D FMCW imaging system

Département d'Optronique (LETI)

Laboratoire Architecture Systèmes Photoniques

01-09-2021

SL-DRT-21-0697

laurent.frey@cea.fr

Photonics, Imaging and displays (.pdf)

Capturing distance information from a scene is becoming a major asset for certain new applications. A typical example being facial recognition by a cell phone. Different techniques already exist with varying degrees of advantages and disadvantages. Within LETI / DOPT we are interested in several techniques and in particular that based on optical frequency modulation (FMCW). The suggested thesis is articulated around the optimization of the reception module of a miniaturized 3D FMCW imaging prototype. The work will be carried out on 3 optical components to be designed/ simulated and optimized in order to improve the final 3D imaging system. The candidate should have extensive knowledge in optics / simulation / instrumentation and laser interferometry.

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Numerical twins of strained interfaces involving MoS2, III-N and SiC obtained by combining experiments and simulations

Département des Plateformes Technologiques (LETI)

Laboratoire

01-10-2021

SL-DRT-21-0699

cyril.guedj@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

This transversal thesis aims at developing realistic and predictive atomic scale simulations to analyze the interfaces between a substrate and the deposited thin film in relationship with the STM and STEM measurements. This numerical tools will be useful to describe the behavior of atoms at the interfaces to understand and optimize the growth processes. To achieve this goal, an approach mixing the speed of machine learning force fields and the precision of ab-initio DFT calculations will be performed. It is necessary to use several level of descriptions adapted to the physical scale: ab-initio methods for electronic effects at the interface and empirical potentials to describe long-scale elasticity. The coupling between scales will enable a faster or deeper atomistic modeling of realistic systems. Two types of growth will be explored to develop this general methodology: a direct growth on MoS2 or a remote epitaxy on SiC. The use of a recently patended methodology will permit the extraction of the atomic positions from experimental data with the best available precision, which constitutes a major advantage in the verification and validation of all simulations by direct comparison with experiment. Ultimately, this thesis will provide numerical twins of materials and interfaces which should be predictive enough to enable realistic in-silico experiments. Hence, these numerical tools will be very useful to rapidly optimize the technology with maximum precision and confidence.

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Study of 3D pattern etch mechanisms for optoelectronic applications

Département des Plateformes Technologiques (LETI)

Laboratoire Gravure

01-10-2021

SL-DRT-21-0702

aurelien.tavernier@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Optoelectronic devices such as CMOS Image Sensors (CIS) require the realization of 3D structures, convex microlenses, in order to focus photons towards the photodiodes defining the pixels. These optical elements are mandatory for the device efficiency. Their shape and dimension are critical for device performances. These 3D structures are currently manufactured in Leti's cleanroom using successive steps of photolithography, photoresist reflow and plasma etching into the optically functional layer. Leti is at the state of the art on an alternative photolithography technics, so-called Grayscale. This process can produce a whole range of 3D structures not available with standard photolithography, such as concave, elliptic, pyramids and asymmetrical shapes. These structures could be used in a large number of application fields, like photonics and micro-displays (AR/VR). Just as CIS application, it is necessary to transfer these 3D patterns in an adapted functional layer using plasma etching. Currently the transfer by plasma etching of 3D patterns obtained with Grayscale photolithography is not well studied in literature. Consequently, this thematic is innovative and has a real benefit. The goal of this PhD thesis is to study and understand the etch mechanisms in order to control the shape and dimension of the transferred structures. The work will be very experimental and will be mainly performed in Leti's 300mm cleanroom. You will have access to a last generation plasma etch tool and numerous characterization technics. This thesis is in collaboration with the photolithography department and in interaction with different teams, such as the silicon platform and application department.

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Optimization of acquisition parameters in measurement and X-ray imaging through simulation

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme

01-10-2021

SL-DRT-21-0723

anthony.touron@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

The objective of the thesis is to set up a numerical method for optimizing acquisition parameters in X-ray control (XR). The DISC is developing a simulation software for Non Destructive Testing which integrates a module for X-ray imaging. This module, which corresponds to a digital twin of the testing, allows the operator to reproduce an experimental configuration (source, detector, geometry and material of the object, defects, ...) in order to estimate the result he would observe on a real acquisition and thus evaluate the sensitivity of the method, the limit of detectability of defects or the impact of different parameters (tension and filtration of the tube, acquisition geometry). To define these configurations, the operator has to rely on his own experience, which results in the realization of potentially non-optimal acquisitions. This limitation related to user dependency is particularly critical in cases where the signal sought is very weak, for example in the identification of a small quantity of element in X-ray fluorescence or for X-ray tomography which involves a large number of acquisition parameters. The objective is to make the experimental measurements more robust, by adding intelligence to the existing simulation tool, which would then integrate a functionality to optimize the acquisition parameters. The PhD student will have to propose a numerical method allowing to define, from a set of simulations, the parameters maximizing a given quality criterion. This criterion will be defined as a function of the targeted testing (typically the amplitude of the fluorescence peak or the transmission rate of the X-ray signal) and validated experimentally.

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Additive manufacturing of biocompatible and bioresorbable microfluidic scaffolds for the development of implantable organ-on-chip (OoC) systems

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Formulation des Matériaux

01-10-2021

SL-DRT-21-0728

sebastien.rolere@cea.fr

Health and environment technologies, medical devices (.pdf)

The technical and biological benefits of several additive manufacturing (AM) processes, for the elaboration and production of microfluidic scaffolds used in the development of implantable organ-on-chip, will be investigated during this PhD project. AM technics should lead to the development of new complex 3D microphysiological systems, more representative of the in vivo environment. Furthermore, the PhD student will also focus on the development of new transparent biocompatible and bioresorbable polymer materials compatible with the selected AM technics, for the substitution of currently used polydimethylsiloxane (PDMS). Finally, the model microfluidic chips, based on the components developed in DRF and DRT labs, will be included in biological assessment campaigns.

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Modelization and characterization of nano-compliant effects for localized epiaxial growth of GaN on Si substrates.

Département des Plateformes Technologiques (LETI)

Laboratoire des Matériaux pour la photonique

01-09-2021

SL-DRT-21-0730

guy.feuillet@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

In order to decrease the dislocation density induced by the different crystalline structures between the nitride semiconductor layers and the Si substrates they are grown onto, one often uses localised epitaxy methods. However, these result in the formation of other defects arising between the adjacent nucleation centers. We have developed an original localised epitaxy method by which the crystallites are deposited onto deformable nano-pilars, allowing these crystallites to join without creating any defect. The PhD work we propose aims at a better understanding, for a better control, of the mechanical and thermo-mechanical processes at pla during this process of nano-pendeo compliant epitaxy. This will be dealt with using finite element models so as to predict the mechanical behaviour of these complex nano-structured systems and by using an ensemble of structural nano-characterization tools for assessing the defect and strain distribution in the materials. From the reduction of defects in the epitaxial layers, we expect to address some of the main issues for a number of important applications related to these nitride materials in the opto- and micro-electronic domains.

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Architectured materials for heat exchangers applied to the energy transition

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Modélisation et Matériaux pour la Métallurgie

01-09-2021

SL-DRT-21-0731

guilhem.roux@cea.fr

Additive manufacturing, new routes for saving materials (.pdf)

This project concerns the eco-innovation of multi-scale 3D printed architectured structures for innovative reactor-exchangers. The ambition is to improve the performance in terms of kinetics, stability and selectivity of chemical reactions used in the field of hydrogen production or recovery. The structures developed will be optimized by thermal simulation in order to maximize their efficiency by taking advantage of additive manufacturing geometric possibilities. The targeted applications are the production of synthesis gas by catalytic processes: CO2 methanation [1], Fischer-Tropsch reactions, LOHC technology or even the decomposition of NH3. As part of the thesis, it is proposed that one of these applications be treated as a priority. Scientific challenges considered during this thesis will be the development of reliable thermo-fluidic simulation tools at the scale of elementary cells (Representative Elementary Volume) by coupling thermal simulation for the solid part and lattice Boltzmann method for the fluid part. Using an upscaling strategy, modeling at the representative scale of the useful reactors sections (mesoscopic calculation) as well as full scale reactors will be carried out using finite element method (Comsol). A screening of elementary structures will be carried out beforehand in order to identify the most suitable structures for each application, using a design tool for elementary structures. Final expectations will feed several circular economy action levers to reduce economic (competitiveness with more compact, more selective exchangers) and environmental impacts (low in energy and material): increase the process efficiency, increase catalyst lifetimes and decrease in ecological impact through a comparative environmental impact analysis (LCA). This thesis will be a collaboration between DAM/Le Ripault and DRT /Liten. The first year of the thesis will be conducted at Le Ripault (Tours) and the last two years in Grenoble.

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Simulation-assisted imaging for SHM by elastic guided waves: tomography and shape derivative

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND

01-10-2021

SL-DRT-21-0738

tom.druet@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Structural health monitoring (SHM) consists of monitoring the health of a structure using integrated sensors. In this context, the LIST is working on innovative elastic guided wave tomography methods to image corroded areas on simple geometries: plates and pipes. The subject of the thesis is to adapt to our dynamic problems the so-called "shape derivative" method, mostly used on static data, to develop iterative imaging based on a digital twin. This method allows a high quality reconstruction for complex geometries but implies the resolution of an expensive numerical problem. The quality of the image is also highly dependent of the initialization, often based on the perfect structure, potentially far from the current state. We propose to address these two points as follows: the initialization of the shape derivative will be obtained from existing tomography methods, thus improving convergence by limiting the number of iterations and the effect of local minima. The numerical problem will be solved using transient high-order spectral finite elements, allowing fast and inexpensive computations. The definition of metrics suitable for the comparison of simulated and experimental signals will also be part of the study. The performances will be validated on experimental data representative of complex cases of interest for the industry (corrosion under support, etc.).

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Deep learning based approach for sparse view Computed Tomography

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme

01-10-2021

SL-DRT-21-0739

caroline.vienne@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

X-ray Computed Tomography (CT) is a well-established contactless and non-destructive inspection technique for various industrial parts. With its roots in the medical field, it evolved to industry as a key tool for three dimensional (3D) characterization and inspection. Industrial progress brings the need for verification of various objects on the production line and in some cases only the X-ray CT can fit the requirements. However, CT scans take a long time, often up to several hours, to achieve reasonable data quality. This long scanning time is an obstacle preventing integration of CT into production lines. In-line tomography implies strong constraints on the acquisition geometry, such as limited angle and sparse views configuration. Due to missing data, reconstructed images suffer from important artifacts that make the inspection task difficult. Many approaches have been investigated to reduce such artifacts. Among them, those based on deep learning seem promising. Convolutive neural network such as U-Net and generative adversarial networks will be studied for recovering missing projection data. The considered framework consists in creating a first version of the sinogram by projecting the initial reconstruction obtained by a FDK algorithm applied to the original incomplete projections and using the complete sinogram as target. Following the previous training of the neural network on simulated data, a second step will consist in using a transfer learning approach and experimental data sets to train a model able to deal with real data.

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Generative surrogates for tomographic problems based on stochastic simulation

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Modélisation et Simulation des Systèmes

01-07-2021

SL-DRT-21-0744

thomas.dautremer@cea.fr

Artificial intelligence & Data intelligence (.pdf)

While the primary object of stochastic simulation is to allow random generation of complex phenomena from a configuration of parameters (forward simulation), its interest may also lie in the inverse problem: determining a configuration of the model parameters allowing generation of data sufficiently close to those observed experimentally. Thus, tomographic reconstruction (CT / PET) problems are classic representatives in radiation physics. In the usual algorithms in X/gamma-ray imaging or in tomography (CT, PET, muons, etc.), the diffusion phenomena or energy dependence occurring at the level of the scene to be imaged cannot be taken into account in a pre-computed system matrix and require approximate post-hoc corrections. In this study, we propose to embed a stochastic particle-transport simulator in the reconstruction process for achieving integrated consideration of the artifacts of the imaging system. We therefore propose to follow a Bayesian framework in order to guarantee a rigorous management of the statistical uncertainties inherent to experiments and stochastic simulations. However, this Bayesian objective comes up against a major difficulty in Monte Carlo simulation: we do not have a likelihood function but only the capacity to generate observables. To overcome this problem, we propose to learn a local emulator of the simulator, aka a generative surrogate conditioned by experimental observations [1]. The control of computational burden being essential, we will focus on deep invertible architectures [2] allowing forward simulations with few primary particles. On the other hand, to limit the number of simulations the learning database will be built adaptively by active learning [3]. In addition, this approach allows a reconstruction on a vectorized (meshed) space rather than on a fixed grid of voxels, the a priori of reconstruction then corresponding to a random mesh. Bayesian reconstruction on manifolds via a so-called "digital twin" would thus constitute a notable advance in particle imaging / tomography. [1] Cranmer, K.; Brehmer, J. and Louppe, G. The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, National Academy of Sciences, 2020 [2] Radev, S.; Mertens, U.; Voss, A.; Ardizzone, L. and Köthe, U. BayesFlow: Learning complex stochastic models with invertible neural networks. arXiv:2003.06281, 2020 [3] Järvenpää, M.; Gutmann, M. U.; Pleska, A.; Vehtari, A. and Marttinen, P. Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation. Bayesian Anal., International Society for Bayesian Analysis, 2019, 14, 595-622

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Multi-architecture programming for high performance passive tomography reconstruction

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Développement Informatique

01-03-2021

SL-DRT-21-0745

hamza.chouh@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Structural Health Monitoring is a set of non-destructive testing methods that aim to be directly integrated within the structures to control. This allows for continuous structural testing without disabling any equipment or involving additional human resources or testing hardware. Passive ultrasonic tomography uses structural noise of specimens to monitor their thickness. This method is dedicated to specimens that can be described as wave-guides. It is particularly useful to detect corrosion and erosion defects. The testing process implies dealing with large amount of numerical signals and applying various algorithms to them. In order to embed SHM controls into lightweight and low energy equipment, this thesis aims the development of a full passive ultrasonic tomography toolchain. This will require the evaluation of several hardware architectures (GPU, Low power GPU, FPGA) to find the best-suited ones. To this end, the student will produce high performance implementations of the tomography algorithms on the selected architectures and compare them with the performance obtained via a generic approach like Sycl. The development process should be flexible enough to provide maintainable and scalable software in order to ease the integration of future evolutions of the passive ultrasonic tomography method.

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New approach for advanced logic devices contact plasma etching

Département des Plateformes Technologiques (LETI)

Laboratoire Gravure

01-10-2021

SL-DRT-21-0747

francois.boulard@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

CEA-Leti and partners are actively developing quantum computing based on silicon platform. While the interest of such technology is to be compatible with industry, some processes require improvements to achieve sub nanometer control. Among them contact etching is challenging. On billions of contacts etch simultaneously, the lateral critical dimension and the over etch of the stop layer should be mastered to reach dispersion below 1nm and less than 1nm consumption, respectively. To answer to these challenges, we propose to implement gas pulsing on fluorocarbon based plasma etch chemistry. Such approach cycles deposition of a reactive layer and its activation. While it allows theoretically to reach contact etching requirements, its practical demonstration should be done. The work will be mainly experimental and will take place on a 300mm clean room facility. First, experiments on silicon oxide and nitride blanket wafers will be carried out in order to understand the impact of gas pulsing on etch rates. The fluorocarbon reactive layer composition will be characterized by X-ray photoelectrons spectroscopy in order to improve the plasma surface understanding and to optimize the pulsing strategy. Second, learnings will be applied on contact patterned wafers. Cross section scanning electron microscopy and transmission microscopy will be use to evaluate the amount of aspect ratio dependent etching and stop layer consumption. These informations should confirm or reverse proposed mechanisms. Also, an exploratory study will target to quantify charged particles trapped at the contact bottom by capacitance voltage measurements. The final output of the work will be to implement contact plasma etch processes with gas pulsing on real qu-bits demonstrator wafers.

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Characterisation of microstructural effects related to the smartcut of SiC

Département Composants Silicium (LETI)

Laboratoire Intégration et Transfert de Film

01-11-2020

SL-DRT-21-0749

christelle.navone@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

The automotive industry, with the recent expansion of the electric vehicles market, requires wide band gap devices, like SiC, that are more powerful and robust than the Si equivalent devices. However, SiC substrates are still very expensive and their supply is limited by the American and Asian monopoly of SiC wafer fabrication. The aim of this thesis is to create an innovative SiC substrate based on Smart Cut? technology : transfer of a thin SiC monocristalline layer on a cheaper substrate, typically SiC polycristalline wafer. The objectives of this research work are : - To deepen the understanding of SiC surface phenomena as function of the heat treatment of the wafers. This study will be based on annealing tests coupled with substrate surface analysis (AFM, PEEM, Raman, XPS) allowing the surface reconstruction mechanisms to be determined as function of the process parameters. The conditions of Graphene formation on the surface of the substrate should be particularly investigated in order to identify the solutions allowing to limit the SiC topological modification. - To understand the nature of SiC defects induced by the Smart-Cut technology step. The determination of the defects will be based on physico-chemical analysis such as photoluminescence, cathodoluminescence, etc. Chemical analyses based on KOH can also be used to reveal the defects on a larger scale. The propagation kinetics of these defects and their impact on the electrical conductivity will be investigated.

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Multi-functional material for catalytic hydrogenation of CO2 to methanol and dimethyl-ether

Département Thermique Conversion et Hydrogène (LITEN)

Laboratoire réacteurs et procédés

01-09-2021

SL-DRT-21-0750

albin.chaise@cea.fr

Additive manufacturing, new routes for saving materials (.pdf)

Hydrogen production by water electrolysis coupled to catalytic hydrogenation of CO2 into hydrocarbon or oxygenates of high energetic Density (methanol, DME) can contribute to the decarbonation of transportations (sea, air transportation) and provide products for a sustainable chemistry. However, these reactions are limited thermodynamically. The current PhD proposal aims at developing a coupled system of catalytic reaction (CuZnO/Al2O3 and zeolites), de-hydratation by separation and/or adsorption of water (ZSM5 and LTA zeolites) for direct synthesis of methanol and DME form CO2. Non-critical and recyclable material (Cu, Zn, Al) and low environmental footprint processes (supercritical CO2, hydrothermal, micro-waves) or bio-templates. One essential goal will be to obtain 3D oriented zeolites with limited defaults, first on planes surfaces then on 3D structures (ceramics or metal). The intrinsic material performances will first be tested on samples. Then the material will be integrated in a 3D reactor with catalysts at the scale of a few L/min of reactants.

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Development of innovative algorithms for creating non-intuitive masks using neural networks for grayscale lithography

Département des Plateformes Technologiques (LETI)

Laboratoire

01-10-2021

SL-DRT-21-0751

SEBASTIEN.BERARDBERGERY@cea.fr

Numerical simulation (.pdf)

The realization of micronic 3D structures makes it possible to manufacture key functional elements of microelectronics such as micro-lenses for optical imagers. These lenses can in particular be produced using a resin flow process or by gray-scale lithography (grayscale). Grayscale lithography offers the advantage of being able to create structures of different topographies in a single process step. Its success strongly depends on the correctness of the modeling of the process and of the optimization strategy of the optical mask. Grayscale lithography has been developed at CEA-LETI over the past 3 years as part of an industrial collaboration [1]. These developments have made it possible to produce state-of-the-art results [2]. CEA-LETI wishes to continue this research work towards new design and data preparation methodologies. Artificial intelligence and neural networks in particular open up a wide range of possibilities in this area. A first promising study has been carried out in this direction, and shows the full potential that such a technique can offer for the creation of masks, if we break free from classical algorithms. The emergence of multi-beam electronic writing tools for the manufacture of optical lithographic masks makes it possible now to consider the use of curved shapes. It is therefore possible to think about changing the algorithms to use non-regular shapes on the mask for the search for ideal optimal solutions. Necessary during the learning stage, the modeling of the lithography process will also be an important part of the thesis and will follow on from previous work [1]. [1] Thesis of P. Chevalier, Study of a 3D micro-fabrication method for microlenses imaging application (2021) [2] P. Chevalier et al., Rigorous Model-Based Mask Data Preparation, IEEE JMEMS (2021) Applications have to be sent to: Sébastien Bérard-Bergery : sebastien.berardbergery@cea.fr Loic Perraud : loic.perraud@cea.fr

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Numerical simulation for processing powder bed additive manufacturing

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Modélisation et Matériaux pour la Métallurgie

01-09-2021

SL-DRT-21-0752

guilhem.roux@cea.fr

Additive manufacturing, new routes for saving materials (.pdf)

The project concerns the study of powder spreading in the context of powder bed additive manufacturing processes, in particular L-PBF (Laser-, Powder Bed Fusion) and MBJ (Metal Binder Jetting) processes. The ambition is to give CEA a reliable simulation tool making it possible to reproduce what happens during this process key stage, when the real powder bed (intended to be melted or agglomerated depending on the technologies) is spread out. This project will be fed by results from a dedicated instrumented spreading set-up as well as by elementary experiments. The simulation will be based on DEM method (discrete element method, [1]), benefiting from developments acquired by the partners (DES/IRESNE) in powder transitics and from first developments in progress at DRT / LITEN. The particle interaction behavior models will be fed by a wide range of real characterizations under elementary flow conditions. The models will then be initially compared on these elementary tests, then ultimately on real full-scale results obtained on the specific DRT/LITEN spreading set-up. Today, several works are carried out on this subject ([2] [3] [4] [5]), but they take into account idealized, spherical and mainly monodisperse powders. The originality of this work compared to the state of the art is to investigate beyond the behavior of model powders by taking into account real morpho-physico-chemical state of various powders (surface roughness, sphericity, charge electrostatic effect, effect of humidity, effect of oxidation state,?) of powders. In particular, one objective will be to understand the mechanisms powders ageing and their consequence on flowability, a real industrial issue. In addition, this study will show the consequences on the flowability of composite powders developed at CEA ([6] [7]). This thesis will be a collaboration between DES/IRESNE and DRT/LITEN.

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Integration of RF switches based on chalcogenide phase change materials

Département Composants Silicium (LETI)

Laboratoire Composants Radiofréquences

01-10-2021

SL-DRT-21-0754

bruno.reig@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

In order to meet the future needs of 5 / 6G cellular networks and SATCOM satellites, It is necessary to develop RF systems with higher performances and agility. In this context, new RF switch technologies based on chalcogenide phase change materials have gain strong attention as they promise to offer disruptive solutions to realize miniature and high speed reconfigurable RF circuits with low power consumption and that can be easily integrated with CMOS circuits. The objective of the PhD thesis is therefore to develop a new RF switch technology based on chalcogenide materials for future wireless telecommunications systems. The requested work is multidisciplinary and will be carried out in close collaboration between three division of the CEA-LETI bringing their expertise on the synthesis of new materials, on the technological integration of innovative RF components and on the development of advanced electronic functions. The PhD student will define the main specifications of the switches for the targeted applications and will seek to identify the key properties required for the phase change material. He will evaluate various alloys of chalcogenide materials and will develop a technological flow for switches in order to optimize the reliability and the performance of the component. Finally, he will design innovative RF circuits and he will study the influence of design parameters on the overall system performance within an application demonstrator.

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DNA based 2D qbits network

Département des Plateformes Technologiques (LETI)

Laboratoire

01-10-2021

SL-DRT-21-0759

raluca.tiron@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

In nanotechnology in general and semiconductor industry in particular, there is an ever increasing need for smaller and more complex features at an ever lower cost. Some examples of applications are sub-10 nm features for creation of FinFETs, lateral (horizontal) and vertical gate-all around nanowires, single electron transistors and advanced non-volatile memories (STT-RAM, MRAM, OxRAM, etc.). To address the challenge of patterning at sub-10 nm features novel patterning approaches must be envisioned. DNA (deoxyribonucleic acid), by virtue of its inherent small diameter (2 nm), tendency to self-organize into various different morphologies and its possibilities for functionalization, offers the possibility to realize both two- and three dimensional structures at nanometer scale. The goal of this PhD work is to demonstrate the feasibility of nanostructuring the surface of a substrate using DNA origami as a mask, with an ultimate resolution of a few nanometer and with a density that is above the current state of the art in semiconductor industry. The focus of the thesis will lie on ever more complex features, while ensuring long-range order by conventional lithography guide patterns. The targeted demonstrator consists of manufacturing a 2D network of qbits based on DNA origami self-assembly.

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Use of polymers for the transfer of single-crystal thin films

Département des Plateformes Technologiques (LETI)

Laboratoire

01-04-2021

SL-DRT-21-0761

pierre.montmeat@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Organic polymers are versatile materials: they are good insulator, are easy to process for the elaboration of thin films and exhibit a high thermal resistance. For these reasons, they can be used for the elaboration of substrates for microelectronic purposes. In this field, the PhD work aims at the elaboration of innovative structures in which a single-cristal semi-conductor is attached to a polymer.

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development and characterization of conformable piezoelectric materials for medical application

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire Composants Organiques

01-10-2021

SL-DRT-21-0767

mohammed.benwadih@cea.fr

Health and environment technologies, medical devices (.pdf)

Recent advances in materials, manufacturing, biotechnology, and systems have favored many sensors and actuators based on flexible, biocompatible piezoelectric materials in medical fields.In this internship, the principles, the future opportunities and challenges in the development and characterization of conformable piezoelectric materials for medical use will be examined. An expandable piezoelectric sensor / actuator, made on a stretchable substrate, will be developed with materials (composites / polymers) deposited by printing methods. These developments will make it possible to study the feasibility of using such piezoelectric components in the field of medicine. The PhD student, with the teams in place, will develop a piezoelectric component on a stretchable substrate, via (i) the use of an intrinsically stretchable piezoelectric polymer or via (ii) the implementation of composite materials (inorganic piezoelectric particles in a matrix polymer). The intern will also have electrical characterization work to be carried out on these components. This internship will take place as part of a collaboration between the LGEF laboratory of INSA LYON for piezoelectric characterizations and CEA_Liten for material choice / process development aspects and material characterization.

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Conception and test of innovative OSL/FO detectors for online neutron monitoring dedicated to dismantling-decommissioning and post-accidental management

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-10-2021

SL-DRT-21-0771

sylvain.magne@cea.fr

The LIST Institute (CEA/Saclay) has developed an innovative fiber-coupled photon dosimeter based on Optically Stimulated Luminescence (OSL), successfully applied on several dismantling sites (Marcoule and Cadarache, France). Radiological investigations in hard-to-access zones (tanks, pipes, reactors, storage ponds, etc.) are performed online with miniaturized OSL/FO probes, as a substitute to conventional dosimeters (gamma camera, CZT, GM, etc.). Operators are in demand for an online neutron dosimetry for the plutonium line or for post-accidental management, necessitating an important research effort on probe conception. First, a review of the literature (papers, patents) will be provided and two sensor conceptions will be investigated at DM2I (SAC) and DER (CAD) with the help of Monte-Carlo codes (e.g. MCNP, TRIPOLI-4), dedicated to the detection of thermal and fast neutrons respectively. Commercially available materials or specially designed ones will be investigated for thermal neutron detection. Furthermore, as photon irradiation is ubiquitous, neutron contributions will be discriminated against photon ones, possibly by making use of two detectors of unbalanced sensitivities. Then a preliminary series of detectors will be manufactured based on Monte-Carlo modelings and tested under gamma and neutron expositions for several energies with the OSL/FO readout units available at DM2I/SAC. Several neutron sources are available at DM2I/SAC (SAPHIR), DER/CAD or at IRSN/CAD (AMANDE). Conclusions will be drawn from the experimental feedback about the performance of the new OSL/FO neutron detectors and their ability to discriminate photon vs neutron contributions. Taking into account this feedback, a second series of detectors will be manufactured and eventually tested in common mode (photon and neutron), in order to validate their field use. Papers and communications will be provided throughout the PhD period, after possible filing of patent(s) and ahead of the writing of the PhD dissertation.

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Multi-criteria thermo-economic analysis and optimisation of complementarities between CO2 (CCU), biomass (BE-CCS) and the electrical vector (H2 production, battery storage), within the framework of the French and European energy mixes.

Département Thermique Conversion et Hydrogène (LITEN)

Laboratoire des systèmes énergétiques pour les territoires

01-10-2021

SL-DRT-21-0778

guillaume.boissonnet@cea.fr

Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Within the framework of studies on the closing of the carbon cycle, it is essential to look at the complementarity between several sources of carbon (CO2 and biomass) and sources and vectors of decarbonated energy (electricity, H2). This constitutes a complex system including many variables and constraints, which it is necessary to study with means of optimization and multi-criteria analysis, in order to make the most of it. The thesis will be articulated around 3 axes - Elaboration, consolidation of a methodology for optimization and multi-criteria analysis of energy systems including non-fossil carbon as a material resource and decarbonated energies. - Development and consolidation of a database of technological solutions including non-fossil carbon as a material resource and decarbonated energies. - Study of several systems according to the philosophy of the scheme: coupling carbon and electricity, in particular through hydrogen.

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Area selective deposition of oxydes for microelectronic

Département des Plateformes Technologiques (LETI)

Laboratoire

01-10-2021

SL-DRT-21-0781

chloe.guerin@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

In order to reduce the manufacturing costs of integrated circuits and continue their miniaturization, disruptive approaches based on the use of selective deposition processes are now being considered in addition to photolithography. Recent developments are mostly linked to the use of atomic layer deposition (ALD) which is a very suitable technique for the development of a selective process due to its high sensitivity to surface chemistry. ALD is a thin film deposition method based on the self-limited adsorption on a surface of gas phase precursors and surface reactions between precursor molecules and a reagent, allowing atomic-scale control of the thickness and quality of the deposited material. The objective of this thesis concerns the development of a localized selective deposition (ASD for Area Selective Deposition) based on the use of an organic layer allowing the deactivation of surface chemical reactions in ALD. This organic layer should act as an inhibition layer of ALD which allows selective deposition by zone. In the literature, this approach generally uses self-assembled monolayers (SAM) which may have limitations in terms of density and thermal or chemical stability. In this project, we will focus on the development of inhibition thin films deposited by vapor-based processes with the aim of finding a versatile method to allow the selective deposition of metal oxides. In addition to the selectivity with ALD deposits, the selection criteria will be the thermochemical stability of the inhibition layer in order to support the ALD process conditions as well as the possibility of depositing thick oxide layers. During this thesis, the PhD student will have access to several deposition techniques (ALD, PECVD, iCVD) as well as to a rich nano-characterization platform (ellipsometry, FTIR, contact angle, AFM, XPS, Tof-SIMS). These surface analyzes and thin film characterizations will allow to identify the best approach in order to obtain the highest possible selectivities. Fine characterization of organic and inorganic films at the nanoscale will also be carried out on patterned structures. One objective of this work will be also to highlight the mechanisms at the origin of selectivity as well as defect formation. Finally, the ASD process will be implement for the realization of an (opto) electronic demonstrator.

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Integration of Teraherts III-V transistors on silicon substrate for submillimeter wave applications

Département Composants Silicium (LETI)

Laboratoire d'Intégration des Composants pour la Logique

01-10-2021

SL-DRT-21-0796

herve.boutry@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Wireless technologies have advanced by leaps and bounds over the last several decades. Speeds have increased dramatically, connectivity has improved, and wireless network protocols, including Wi-fi and cellular, have become ubiquitous. Yet, despite a reasonnably fast, persitent wireless connection available in most parts of the world, today's wireless networks are still relatively limited in terms of how they can handle large volumes of data. The need for bandwith is growing and Terahertz communications, at frequencies of 300Ghz, could adress this problem. Multichip modules may present a possible solution to the mmW integration problem but at these frequencies, the lateral arrangement of chips on a carrier substrate comes at the cost of transmission line and contact losses. A preferred arrangement is the vertical 3D integration of mmW semiconductor devices or building blocs on top of the CMOS chip which could leverage the electrical specifications of the combined circuits.

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Quantum computing for logistics and industrial applications

Département Ingénierie Logiciels et Systèmes (LIST)

Labo. ingénierie des langages exécutables et optimisation

01-03-2020

SL-DRT-21-0797

florian.noyrit@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

Quantum computing sounds promising to solve computational problems that classical computing cannot address practically because of their complexity. However, despite its promises and the recent development of quantum technologies, industrial applications of quantum computing are so far limited. Nevertheless, recent developments of some quantum algorithms (e.g. Variational Quantum Eigensolver [1], Quantum Approximate Optimization Algorithm [2]), running on existing or upcoming quantum devices (NISQ - Noisy Intermediate-Scale Quantum) [3], suggest many opportunities for near/mid-term applications in solving some optimization problems. Logistics and industrial engineering are application fields that offer optimization problems (scheduling, planning, routing?) complex to solve with classical algorithmic. Some theoretical analysis and early experiments [4] already draft some viable applications for quantum computing techniques. However, because it is a living research topic, knowledge on these topics is scattered, unstable (new algorithms are proposed frequently), sometimes speculative, and not generalized yet. We therefore propose to explore the application of recent quantum computing techniques (notably hybrid algorithms and NISQ-applicable) on some optimization problems from our industrial projects. The objectives of this research work will be the followings: ? Select relevant optimization problems from our ongoing or past projects in fields of logistics and industrial engineering. ? Select quantum algorithms applicable to those problems from state of the art and state of the practice and implement them. ? Adopt or devise a benchmarking framework that can evolve along with the progresses in the field of quantum computing-based optimization. : computing time optimization, problems size, computer size ... ? Evaluation of the technical viability through concrete experiments. The evaluation will notably aim at analyzing the applicability factors such as the convergence properties of algorithms, the impact of the formulation of the problem on the effectiveness, the influence of the hardware architecture. More generally, the evaluation must give insights on the qualitative and quantitative thresholds (number of qubits [5], connectivity, noise?) that make the algorithm viable on NISQ devices (existing or upcoming). ? Propose and develop solutions to make the algorithms viable. For example, by adapting or extending the algorithms, proposing rewritings of the problem formulations, by implementing a particular compilation flows, by adapting the architecture of the execution platform... This work implies the access to actual or emulated quantum computing devices to run the experiments. Experiments are expected to be run on various platforms. This PhD will be carried out at Grenoble. [1] A variational eigenvalue solver on a photonic quantum processor, Peruzzo et Al., 2013 [2] A Quantum Approximate Optimization Algorithm, Edward Farhi and Jeffrey Goldstone and Sam Gutmann, 2014 [3] Quantum Computing in the NISQ era and beyond, John Preskill, 2018 [4] Quantum Computing Algorithms for optimised Planning & Scheduling (QCAPS), Dr Roberto Desimone et Al. 2019 [5] Guerreschi, G. G., & Matsuura, A. Y. (2019). QAOA for Max-Cut requires hundreds of qubits for quantum speed-up. Scientific reports, 9(1), 6903. The candidate should have good knowledege in one or more of the following topics : ? Quantum information and computation ? Combinatorial Optimisation ? Linear Algebra ? Algorithmic Complexity

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Tactile-based learning and classification methods for task planning and verification ? applications to multi-digital and bimanual robotic manipulation

Département Systèmes (LETI)

Laboratoire Signaux et Systèmes de Capteurs

01-09-2021

SL-DRT-21-0803

saifeddine.aloui@cea.fr

Artificial intelligence & Data intelligence (.pdf)

The robotic manipulation of objects first of all requires a grasp planning of these objects, which is a function of the characteristic parameters of the considered hardware tools and the task to be performed (such as the accessibility areas or the level and direction of the efforts that may be involved in the tasks of assembly, insertion, dexterous manipulation, etc.). In addition, during the execution of the task, it is necessary to be able to ensure the nominal progress of the planned task, by detecting the occurrence of certain critical events necessary for its completion (such as the interaction of objects with each other, the loss of stability of the object, etc.) and then validating the actual completion of the planned task (via the classification of data that characterizes the success or failure of tasks such as insertion or assembly). These detection and verification steps, which are crucial when it comes to robotizing certain critical tasks requiring a high level of traceability, can be based in particular on the analysis and monitoring of data or signals specific to the handling system in question. The work requested will exploit an experimental system consisting of a two-handed station, equipped in particular with two multi-digital grippers equipped with multimodal tactile sensors developed by the CEA. This thesis work is essentially divided into two parts. The first part consists in the use of learning methods, which are able to take into account the capacities of the pluridigital manipulators and the imperatives of the task, to plan the grasping of the objects. The second part of the thesis aims at exploiting some methods based on the classification of tactile and proprioceptive signals of the system to validate the accomplishment of the task.

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Edge-IA autonomous systems for biodiversity protection

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs

01-10-2021

SL-DRT-21-0808

esteban.cabanillas@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

Within the framework of technological development for biodiversity protection, measurement tools to precisely quantify the impact of human activities (agriculture, wind turbines, antennas, urban sprawl, etc.) and strategies implemented for biodiversity protection have yet to be developed. The proposed thesis aims to overcome this lack by proposing to develop an autonomous and reliable on-board electronic system for monitoring and quantifying biodiversity. The thesis will be based on advanced technological solutions using edge artificial intelligence (edge AI), energy harvesting and energy management (photovoltaic modules, battery, energy management circuit), data processing from various sensors (audio, video) and low-power electronics (hardware and firmware), particularly for data processing and communication aspects. The core of the thesis will therefore focus on minimising the hardware and firmware energy consumption of embedded electronic systems implementing artificial intelligence for the application "monitoring and protection of biodiversity". A complete electronic device (hardware + firmware) implementing these innovations and deployed in real situations is expected by the end of the thesis.

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novel integrated circuit topologies using innovative capacitive components on silicon

Département Composants Silicium (LETI)

Laboratoire Stockage et Microsources d Energie

01-10-2021

SL-DRT-21-0814

sami.oukassi@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

The objective of this thesis is to assess the potentiel of hybrid silicon capacitors developed at LETI as components in novel architectures of integrated energy conversion circuits. The hybrid capacitors exhibit a combination of unique properties in terms of energy density (ionic storage of the order of 40 mJ / mm3) and frequency response (dielectric storage demonstrated up to 30 GHZ), in addition to a technological realization on 200 mm silicon wafers. Within the framework of this project, it is proposed to design energy conversion circuits (eg. DC-DC converters)exploiting the intrinsic properties of the hybrid capacitors developed at LETI.

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Attention guided dynamic inference in perception neural networks for autonomous mobile systems

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Intelligence Artificielle Embarquée

01-10-2021

SL-DRT-21-0816

karim.benchehida@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Autonomous mobile systems are becoming more and more present in a variety of domains such us the delivery, inspection or agriculture executing tasks of growing complexity. These systems need to have a precise positioning (localization, pose estimation) and environment perception (detection, classification, object tracking?) to take relevant navigation decisions for example. To be very efficient, recent state of the art approaches based on neural networks for these perception tasks tend towards the use of wider (in the number of channels and modalities) and/or deeper (in the number of convolution layers) networks with a direct impact on the computation complexity and the decision making latency. The research proposal concentrates on the enhancement of the computational efficiency of perception neural networks (complexity vs. accuracy). For that purpose, we intend to dynamically reduce the computation (via dynamic inference techniques) and focus it (via attentions mechanisms) to allow the use of large representation capability networks that usually cannot be embedded. An important part of this project will be on the implementation of the developed techniques on a real embedded mobile platform to demonstrate the effectiveness of the approach. Indeed, the Embedded Artificial Intelligence Laboratory of the CEA have available some mobile robotic platforms and a fully automated, autonomous ready electrical vehicle with multiple integrated sensors. The research results will enrich the perception modules of these systems.

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Qualitative reasonning and design of complex systems

Département Ingénierie Logiciels et Systèmes (LIST)

Labo. ingénierie des langages exécutables et optimisation

01-04-2021

SL-DRT-21-0823

jean-pierre.gallois@cea.fr

Numerical simulation (.pdf)

The design of complex systems is an activity that affects many industrial and research fields. This implies difficulties in modeling and simulating by the heterogeneous nature of the data involved, with discrete and continuous aspects. Two approaches are possible. Quantitative methods, whose analyzes are numerical, are the most used: their results are precise but they consume a lot of time and resources. Qualitative methods are based on a symbolic interpretation of the models, and can be used without knowing all the numerical parameters, by relying on dependency relationships between variables. They are less precise but they can be applied very early in the design phase and can be used to plan numerical simulations according to the objectives and to improve the results of analyzes (proofs, optimization, etc.). The work already carried out at the LIDEO laboratory of CEA LIST on modeling and qualitative simulation will be extended by the integration of concepts from naive physics and common sense reasoning to lead to an approach closer to engineering concepts. The results will be used for modeling, simulation but also for optimization on case studies representative of industrial examples.

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Exploring learning techniques for "Edge AI" taking advantage of Resistive RAM

Département Systèmes et Circuits Intégrés Numériques

Laboratoire Systèmes-sur-puce et Technologies Avancées

01-09-2021

SL-DRT-21-0825

Francois.RUMMENS@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Today's computer architectures are inefficient in handling the simulation artificial neural networks, hindering their application in power-constrained environments, such as edge computing and the Internet of Thighs. Dedicated hardware implementations of neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power processing suitable for edge computing. These new circuits and technologies have the potential to endow the system with the ability to learn at the edge. This breakthrough, which is unattainable using conventional approaches, can have many advantages, as it enables adaptation to changing input statistics, reduced network congestion, and increased privacy. However, current approaches often focus on learning algorithms that cannot be reconciled with the non-ideal physical behaviour of resistive memories. This thesis aims at exploring various algorithmic solutions for inference and learning in order to propose neural network architectures more adapted to the reality of the resistive memory technologies developed at LETI.

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Reliability of 3D avalanche photodetectors

Département Composants Silicium (LETI)

Laboratoire de Caractérisation et Test Electrique

01-10-2020

SL-DRT-21-0830

jean.coignus@cea.fr

Photonics, Imaging and displays (.pdf)

STMicroelectronics develops various CMOS-based technologies for imaging. The rise and democratization of image sensors is leading to a diversification of technological uses such as high-resolution imagery and telemetry for domestic and automotive use. One of the challenges is to meet market needs and adapt to the competition by constantly improving the performance and reliability of devices. The objective of this thesis is to study and model the reliability of avalanche photodetectors for single photon detection. The principle of this sensor lies in the ability to measure the transit time between an optical source and the detector, from a few centimeters to several tens of meters while being insensitive to the surrounding light. A matrix made up of thousands of pixels makes it possible to restore a faithful 3D image of the target. To date, first tests show that the detector degrades over time, leading to a loss of sensitivity and degradation of measurement precision. Quantifying these effects and understanding these drifts is absolutely necessary to improve the manufacturing process and develop a predictive model of reliability. The thesis will focus equally between the reliability of a single pixel and the reliability of a pixel matrix, in order to approach product reliability. The candidate will rely on a set of characterization and reliability measurement tools, as well as modeling and simulation tools developed at STMicroelectronics.

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Integrated circuits for electromechanical tuning of MEMS devices

Département Composants Silicium (LETI)

Laboratoire Gestion de l'Energie, Capteurs et Actionneurs

01-09-2021

SL-DRT-21-0837

gael.pillonnet@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

The Phd objective is to explore electromechanical tuning techniques dynamically to modulate the resonant frequency and quality factor of electrically-coupled MEMS devices.

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Beyond Shannon with Semantic Communications for 6G Networks and Services

Département Systèmes (LETI)

Laboratoire Sans fils Haut Débit

01-06-2021

SL-DRT-21-0844

emilio.calvanese-strinati@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

The future of mobile communications will be characterized by ubiquitous connection availability, very dense networks in terms of number of users and access points, ultra-low latency, very high bandwidth, and energetic efficiency. The 6G network revolution will be enabled by cutting-edge technological innovations, concerning millimeter-wave radio communications, baseband and RF architecture, resources virtualization, and the native support of artificial intelligence. The main goal of this PhD investigation is to motivate the need, in the design of new 6G networks, for a paradigm shift from the mainstream research, which basically builds on Shannon's framework, towards semantic and goal-oriented communications. A game-changing idea consists in exploring semantics in wireless communications to go beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted packet, irrespective of the meaning conveyed by the packet. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment. The PhD candidate will explore the very recently-proposed and innovative concept of wireless semantic communications. The work will focus on the design of algorithms for a proactive and dynamic implementation of semantic communications, targeting the optimal end-to-end efficiency of the joint allocation of the above mentioned resources. Distributed learning techniques will be investigated and proposed.

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Compact and Efficient Power Converters Integrated Circuits for Supply-Modulated RF Power Amplifiers

Département Composants Silicium (LETI)

Laboratoire Gestion de l'Energie, Capteurs et Actionneurs

01-09-2021

SL-DRT-21-0846

alexandre.giry@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

The PhD objective is to explore compact and efficient power converter topology integrated nearby RF power amplifier to enhance energy efficiency in 5G/6G context.

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Development of an X-ray phase imaging technique suitable for in-situ tomography of composite materials

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Instrumentation et Capteurs

01-10-2021

SL-DRT-21-0847

adrien.stolidi@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Due to their high strength-to-weight ratios, composite materials are widely used in the aeronautic industry. Because of their structural heterogeneity, composite materials can be affected by complex damage that may appear either shortly during/after their manufacturing or later during their lifetime. However, conventional non-destructive techniques can provide sufficient accuracy to identify microscale flaws, such as matrix cracking and local fibre/matrix debonding, of geometrical dimensions of a few millimetres at the most. X-ray computed tomography (CT) is a powerful tool enabling to resolve microscale flaws and identify them in 3D dimension. However, low atomic number materials produce poor contrast, calling for innovating X-ray approaches such as phase contrast imaging, in demanding conditions for instance the in-line and time resolved monitoring of composite materials during stress fatigue. The goal of this thesis is to develop an innovating in-situ tomography technique based on phase shifting of the X-ray produice by the sample, in addition to the X-ray absorption. A first experience, showing promising results, was developed in the framework of Onera and CEA ? LIST collaboration, based on multilateral shearing interferometry (MSI), a phase measurement technique developed by Onera. The first part of this thesis work will be to reliable the experimental bench and developed adapted in-situ phase tomographic methods in order to compare them to classical attenuation tomographic approaches. The use of non-standard equipment such as robotic tomography platform (CEA Saclay & Nantes) will be part of this thesis work. Representative characterisations will be performed in agreement with the mains goals fixed by Onera specialists in aeronautics material. Time resolved X-ray phase contrast imaging will also be performed with mechanical constraints on sample(volume flaws of size of tens of microns or less). This part will be perform with Onera and CEA in close collaboration with LP3 laboratory (mixt research unit of Aix-Marseille university and CNRS), who as developed an laser-plasma X-ray source with a repetition rate of 100 Hz, based on intense pulsed laser beam. Preliminary experience will be perform in this installation. The second part of this work will be focus on the use of scattering information, encoded in the MSI measurement, in order to retrieved the fibres orientation of the composite sample. Previous work based on tensor tomography reconstruction can be found in the literature where directional information of the micro-structures of the sample can be link to the privileged scattering orientations, in function of the inspection energy used. The LP3 source (Molybdenum K-alpha source) will be used in order to work on the scattering responses at 17.48 keV. The correlation between the scattering signal and the fibres orientation distribution will be based on spherical deconvolution approach. This work will be mainly perform at Onera and CEA laboratories. A theoretical part, focus on interferometry technics and X-ray interaction, will be achieve. Then an important experimental part, with some numerical developments, will be carry on X-ray installations (CEA & LP3). These experimentals developments will lead to an important technological developments with a strong innovating part.

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Understanding the structure and properties of metavalent phase-change materials based on innovative chalcogenide compounds for a technological breakthrough in embedded Phase-Change Memory

Département des Plateformes Technologiques (LETI)

Laboratoire

01-10-2021

SL-DRT-21-0854

pierre.noe@cea.fr

Advanced nano characterization (.pdf)

Owing to their high scalability, short switching time (~ns), Phase-Change Materials (PCM) are very promising for new generations of Non-Volatile Memories (NVMs). For high temperature embedded applications (ePCM), the most promising PCMs are multiphased complex composition alloys (Ge-rich GeSbTe chalcogenide alloys), which raise critical issues due potential unwanted Ge phase separation occurring at crystallization. In that context, this PhD project targets a breakthrough with the study of innovative very high temperature PCM compounds (data retention of the RESET amorphous state >> automotive criteria & soldering reflow thermal budget) without any parasitic phase separation upon crystallization. Recently, a Leti team has proposed a particular Ge-Se-Te composition that is remarkably stable (>250°C for 10 years) in the amorphous state but that also exhibits very interesting crystalline state properties that have not been reported before (no description of the atomistic or electronic structure). The aim of this PhD is to couple advanced structural characterizations (electron microscopy, synchrotron X-ray experiments ?) with modern simulations (AIMD/DFT ) to get an understanding and further master the properties of such new PCMs.

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Study of the persistence in IR MCT imaging retina for earth observation from space

Département d'Optronique (LETI)

Laboratoire d'Imagerie IR

01-10-2021

SL-DRT-21-0855

nicolas.baier@cea.fr

Photonics, Imaging and displays (.pdf)

IR detection is a major stake for ongoing and future space science missions studying the atmosphere chemistry (for instance MicroCarb mission dealing with the measurement atmosphere CO2 concentration) and more generally for science imaging and sensing (for instance the ARIEL mission aiming at studying exoplanet atmospheres). IR detector is a major performance driver for such instruments. The most widely used technology relies on a sensitive layer made of HgCdTe, a narrow gap semiconductor, suitable for IR photon absorption. This sensitive layer is then hybridized onto a silicon read out integrated circuit (ROIC) for multiplexing and signal conditioning. This particular technology has been developed at CEA LETI then transferred to Lynred for qualification and production. The very high level of measurement precision of science missions requires an in depth study of all sources of detection performance degradation. Among various biases is the persistence phenomena: like the human eye looking at the sun, ghosts of previous images are sometime polluting detected images, thus degrading the instrument performance. This effect is most of the time attributed to the sensitive layer (often associated to charge trapping-detrapping phenomenon) and is difficult to calibrate. Ideally, we look for a way to suppress or at least minimize this effect, playing with fabrication parameters. This requires an important work to improve our understanding of the physical phenomenon involved in persistence. Within previous developments with CNES, LETI has built a measurement bench suitable for such fine characterisations. This work intend to study this effect on identification detectors, with regard to different detector fabrication variations. A second step will be to identify the most efficient technological solutions in collaboration with Lynred Company producing such detectors. Last but not the least the validation of the proposed solution shall be performed.

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HIGH PERFORMANCES ELECTROLYTES BY ALD FOR IONIC COMPONENTS

Département des Plateformes Technologiques (LETI)

Laboratoire

01-09-2021

SL-DRT-21-0857

messaoud.bedjaoui@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

This work aims to explore the feasibility of new ionic layers extremely thin obtained by ALD technique (Atomic Layer Deposition). These inorganic solid state layers are major candidates as high performance dielectric for main applications like high density capacitances or synaptic transistors for neuromorphic computing. The preliminary effort will be focused on the intrinsic characterization of ALD-based layers (thickness<20nm) using 2D short loops in order to identify attractive ones in terms of ionic conductivity and electric conductivity in comparison to solid electrolyte reference LiPON obtained by standard deposition technique. One of the challenges consists to adapt these ALD-based layers with 3D structures (high aspect ratio >200). The other challenge aims to reduce the ALD-based ionic layer thickness less than 5nm while still maintaining the advanced electric properties. This work covers multiple aspects including the ALD process, the ALD precursors, intrinsic layer development and technological integration on 3D components. Particular focus will be devoted to the physical-chemical, morphological and electrochemical characterizations of these layers.

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Integrated circuits to enhance the performance of resistive-based memories

Département Composants Silicium (LETI)

Laboratoire de Composants Mémoires

01-09-2021

SL-DRT-21-0859

gabriel.molas@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

The Phd objective is to explore fine and dynamic tuning circuit techniques, integrated nearby memory cells, to enhance the performance of RRAM.

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High frequency inductive coupling wireless power transfer based on GaN

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs

01-10-2021

SL-DRT-21-0862

nicolas.garraud@cea.fr

Energy efficiency for smart buildings, electrical mobility and industrial processes (.pdf)

Wireless power transmission (WPT) technologies are booming with applications in the aerospace, consumer electronics, medical, automotive and defense sectors. The purpose of these technologies is to transmit electrical energy between two elements without using a physical medium with the maximum possible efficiency. Power transmission technology using resonant inductive coupling seems to be the most promising in terms of near-field efficiency. This thesis is part of the development of the thematic on wireless power transmission and power at CEA-LETI in Grenoble. In this context, the objective of the thesis is to study, develop and test the performances of this technology over the VHF frequency range (30-300 MHz) unexploited in the literature, by integrating GaN transistor-based electronics. The candidate will develop analytical and numerical models to optimize the electromagnetic coupler, compare the performances of existing systems in the literature, and propose, develop and test the performances of innovative GaN-based topologies. The final goal of the thesis is the analysis and understanding of the advantages and limitations of this technology compared to the lower frequencies traditionally used. A multidisciplinary profile oriented towards power physics and electronics is sought for this thesis. In addition to a solid theoretical background, the PhD student will need to possess teamwork skills and an aptitude for experimentation. The PhD student will be integrated in CEA-Leti's Systems Department, within teams of researchers with strong skills in the development and optimization of power and wireless power transmission systems.

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Cyclophane-Loaded Plastic Scintillators for Neutron/Gamma Discrimination

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-10-2021

SL-DRT-21-0868

guillaume.bertrand@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

In the field of nuclear detection, neutron and gamma radiations are very hard to differentiate. Only a few formulations of plastic scintillators are able to perform neutron/gamma discrimination, using the phenomenon known as triplet-triplet annihilation (TTA). The CYCLOPS project aims at synthesizing poly-aromatic cyclophanes molecules, which can act as exciton antenna and promote TTA, and incorporating them in plastic scintillators. Our objective is to enhance the discrimination power of plastic scintillators with these new molecules. This proposal is built around encouraging preliminary results that lead to a patent. In order to capitalize on this, we organized the project in three parts: organic synthesis; scintillator manufacturing and testing; photophysical exploration. If successful, application may range from homeland security and non-proliferation to plant monitoring.

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Improvement and understanding of the performance of silicon cell-based solar generators in harsh environments

Département des Technologies Solaires (LITEN)

Laboratoire des applications modules

01-10-2021

SL-DRT-21-0879

romain.cariou@cea.fr

Solar energy for energy transition (.pdf)

The thesis will be carried out at the interface of several laboratories of the Department of Solar Technologies (DTS) of the CEA located in Le Bourget du Lac on the campus of the National Institute for Solar Energy (INES). The objective of this thesis is to improve the resistance to environmental conditions (radiation, e/H+, UV, thermal cycling) of space solar generators based on silicon solar cells, and to better understand the degradation mechanisms of cells/materials associated. By finely controlling the manufacturing of cells (doping, impurity, architecture, etc.) and modules (materials, thickness, architecture, optical trapping, etc.), it is possible to improve the performance of silicon modules at the end of their lifetime while maintaining a competitive price (?/W), 1 to 3 orders of magnitude lower than space III-V modules.

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Combinatorial synthesis of thin film materials by magnetron sputtering as a means to identify new solid electrolytes for lithium batteries

DAQUIT (CTReg)

01-10-2021

SL-DRT-21-0880

frederic.lecras@cea.fr

Electrochemical energy storage incl. batteries for energy transition (.pdf)

The aim of the study is to identify new Li+ ionic conductors of interest for all-solid-state battery (ASSB) applications, using experimental high-throughput screening (HTS) of materials compositions. The HTS approach will be based on combinatorial synthesis of thin film materials by magnetron sputtering and an appropriate set of characterizations techniques able to get a mapping of the chemical, structural, electrochemical properties of the materials deposited on a single substrate. Correlations between the different properties will be determined and will be used to identify the best electrolyte materials for the foreseen application. Combinatorial syntheses will be carried out in a brand new multi-target sputtering equipment integrated in a glovebox, so that a large range material chemistries can be envisaged among oxides and sulfides. This equipement located in CEA Tech Aquitaine facilities (Bordeaux) allows to 'mix' elements sputtered simultaneously from different targets and to generate thin film with composition gradients (materials libraries) at the surface of a substrate. Physical, chemical and structural characterization of the thin film materials (XPS, EDX, WDS, Raman, ICP, LIBS, TEM) will be carried out at ICMCB (Institute for Condensed Matter Chemistry of Bordeaux) and at the nano-characterization facilities (PFNC) at CEA Grenoble.Conduction properties will be primarly determined by impedance measurements (EIS). More in-depth characterizations (solid-state NMR (Li7, B11, Si29, P31,..) for ex.) will be possibly carried out for particular compositions.

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Integration of scalable arrays of quantum dots on silicon

Département Composants Silicium (LETI)

Laboratoire d'Intégration des Composants pour la Logique

01-09-2021

SL-DRT-21-0883

benoit.bertrand@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

Most of the silicon spin qubit demonstrations were made with linear arrays of quantum dots. However increasing the qubit interconnectivity seems necessary in order to implement efficient quantum error correction protocols. In terms of architecture, this implies transitioning to two-dimensional quantum dot arrays. Grenoble is among the leading groups on this topic with the first demonstrations of elementary 2D arrays on GaAs heterostructures at Neel Institute. The proposed study will aim at implementing such arrays on silicon technology and explore ways to transition

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High-speed distributed FBG sensing for SHM applications based on dispersive spectrometry

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-10-2021

SL-DRT-21-0887

sylvain.magne@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

Fiber Bragg Grating (FBG) sensing for structural health monitoring (SHM) is currently strongly limited in capacity and scan rate. Dispersive Bragg Spectrometry (DBS) is identified as an innovative approach to overcome these limitations (improving both capacity and scan rate) while maintaining cost-effectiveness. Until now, DBS is mostly applied to shock physics using high-bandwidth oscilloscopes. The principle is to use highly-dispersive optical media providing Bragg-to-time delay conversion of pulse light signals. As capacity is trading-off with speed, the same readout device may be tailored to every end-user demand. As an application of the DBS for aeronautics (collaboration with Safran), distributed strain measurements along bonded or soldered joints or within complex inaccessible structure parts may be performed with the help of Chirped Fiber Bragg Gratings (CFBG). Sensing lengths are currently 10-cm long, likely to increase in the future. The principle is to record the time-domain interferogram resulting from the interference between light reflected back by a sensing CFBG and by a reference one and to apply Fourier-Transform-based inverse calculation to retrieve the strain distribution along the CFBG. The PhD student will design the CFBG (on-going collaboration between CEA and University of Lille) and will participate in their characterization. Then, he will setup the experiment involving a pulse laser, a pair of CFBGs and high-bandwidth detector and oscilloscope. Preliminary experiments will be done in laboratory under controlled strain distribution and the inverse calculation will be performed and assessed. Meantime, a dedicated time detection electronic circuit will be provided for the project, as cheaper and simpler solution than oscilloscopes. This circuit will be evaluated with PZT actuators and possibly with laser ultrasonics techniques (collaboration with CNRS-PIMM). Finally, the DBS method will be tested in a representative situation, at Safran premises, on real composite parts.

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Innovative materials for microLEDs, a bright and energy-efficient technology for next-generation displays

Département des Plateformes Technologiques (LETI)

Laboratoire

01-09-2021

SL-DRT-21-0889

philippe.rodriguez@cea.fr

Photonics, Imaging and displays (.pdf)

CONTEXT Many industrial and academic players see microLEDs as the most promising emerging display technology. Allowing to reach very high brightness levels, while keeping a high energy efficiency, microLED displays could bring a new wave of augmented reality devices and revolutionize the way we interact with the digital world. CEA-LETI is a world-leader in microLEDs displays technology, and has partnered with a high-profile technology firm in order to bring these promises into a cutting edge product. ABSTRACT Innovative materials will be selected in order to improve key microLEDs properties. They will be elaborated and their physical, chemical, and opto-electronic properties will be characterized. In-depth scientific analysis will take place in order to correlate micro- and nano-scale properties with the materials behaviour, and the impact of the elaboration conditions on those properties will be studied. The materials will be integrated in the microLEDs, and their impact on the device electro-optical properties will be studied. CANDIDATE In order to be successful, the candidate will need a background knowledge of material science, semiconductor physics, as well as understanding of deposition processes and of materials characterization. During this PhD thesis, the candidate will take part in a state-of-the-art scientific technological environment, and will be able to gather cutting-edge knowledge in the promising field of microLEDs. The candidate will have access to attractive conditions as well as to the numerous advantages given by the CEA.

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Preparation characterization and modelling of electrospun gas diffusion layer for PEM fuel cell

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Composants Pemfc

01-09-2021

SL-DRT-21-0892

frederic.fouda-onana@cea.fr

Advanced hydrogen and fuel-cells solutions for energy transition (.pdf)

Several GDL specifications ( thickness, fibers diameter) could be modified by using electrospun as technique to prepare fibers. The influence of the microstructures properties on the gas/liquid management within the GDL is of utmost important topic for improving the output power of PEM fuel cell. It was reported that at least 50 % of gas transport losses were assigned to the GDL. This impact is even more severe at higher current density (> 3 A/cm²). At such regime the liquid water prevent the gas for reaching the catalyst and the voltage falls sharply. This phenomenon is known as ?flooding effect?. A better understanding of the relationship between macro properties/local properties/electrochemical performances will considerably improve ours insights on this technology. The scientific work will be based on two pillars: 1 - Preparation and characterizations (SEM, electron conductivity, Thermal and gas diffusion) of the electrospun carbon layer 2- Using already available (Matlab/Simulink) or Pores Network Modelling (PNM) tool to link local properties with effective transport properties. Then ijn a second step to connect the effectives transport properties of the GDL to PEM fuel performances model.

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Digital twin of the smart electricity grid by real-time simulation coupled with experimentation (hardware-in-the-loop): methodology and applications

Département des Technologies Solaires (LITEN)

Laboratoire Systèmes Electriques Intelligents

01-10-2021

SL-DRT-21-0894

tran-the.hoang@cea.fr

Smart Energy grids (.pdf)

This thesis project is concerned to try the challenge with innovative techniques such as real-time simulation, co-simulation and hardware-in-the-loop. The main objective is to propose a methodology for the implementation of the digital twin (DT) for applications in cyber physical energy system (CPES). The thesis subject consists of three main scientific locks that we propose corresponding research and development avenues: 1 / Development of simulation models for a multi-scale / multi-physical CPES, in particular through the use of new digital technologies such as artificial intelligence (AI) and data modeling. 2 / Coupling between the DT and the physical experimentation environment by real-time simulation of HIL / PHIL and coupling with a SCADA system. 3 / Development of innovative applications with DT for the CPES.

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Mmwave multi-static scattering model for imaging and radar application

Département Systèmes (LETI)

Laboratoire Antennes, Propagation, Couplage Inductif

01-09-2021

SL-DRT-21-0895

raffaele.derrico@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

This PhD program is proposed within the framework of CEA-LETI's R&D activities in the field of wireless and radar transmission technologies using millimeter waves. These technologies, already used in automotive radar applications and envisaged in a future deployment of 5G, exploit very wide bands. In the long term, the convergence between communication and radar (RADCOM) will make it possible to envisage new applications of precise imagery, reconstruction and "sensing" of the environment. The use of multi-antenna millimeter wave technologies, compacted in a reduced volume, will allow new capabilities in terms of temporal precision and angular resolution. However, the development of these new approaches requires a precise knowledge of the targets backscattering seen by the different antennas. In particular, in short-range applications, the concept of Radar Cross Section (RCS) could be no longer applicable and may require near-field modeling. The objective of this thesis is to develop a millimeter wave backscattering model of objects for proximity radar and multi-sensor imaging applications. The study will begin with a state of the art concerning multi-antenna radar systems and the implementation of a (simplified) propagation model. Then, the PhD student will develop a test bench dedicated to characterization. It will provide composite reflectivity models for different objects and for the human body. This modelling could be eventually based on a point cloud representation that will be combined with artificial intelligence (AI) approaches. The PhD student will be part of the Antenna, Propagation and Inductive Coupling Laboratory at CEA-LETI, in Grenoble (France). He/she will benefit of the state of the art facilities (channel sounders, emulator, OTA setup, and electromagnetic simulator). Application: The position is open to outstanding students with Master of Science, ?école d'ingénieur? or equivalent. The student should have specialization in the field of telecommunications, radar, microwave and/or signal processing. The application must necessarily include a CV, cover letter and grades for the last two years of study.

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Ultra Low Power and High Performance Microphone Signal Processing for Speaker Localization and Auditory Attention Detection : Application to Next Generation Hearing Aids

Département Systèmes (LETI)

Laboratoire Signaux et Systèmes de Capteurs

01-10-2021

SL-DRT-21-0898

vincent.heiries@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Located on the MINATEC campus in Grenoble, CEA-Leti's main mission is to create innovation and transfer it to industry by generating research results that will be used in industry in the medium and long term, positioning its research between academic research and industrial R&D. Within LETI Systems Department, the mission of the Sensor Systems and Electronics Service is to design and produce innovative systems to meet the needs of industrial innovation in a wide range of fields, from the automotive industry to sports and the building industry. The skills involved range from electronics to physics, electromagnetism, magnetostatics, signal processing and applied mathematics. Hearing loss is a major public health problem, affecting about 10% of the world's population. This handicap has a strong impact on the comfort of patients who suffer from it, in many aspects of their lives. Furthermore, with increased stimulation of our hearing system over long periods of time through various digital uses, the trend of increasing prevalence of hearing loss is clearly on the rise. Many forms of hearing loss can be treated through the use of hearing aids that significantly improve the lives of millions of people with hearing loss around the world. These hearing aids have benefited from considerable efforts to improve the underlying technologies in recent years, and today offer very high performance in terms of audio signal quality, amplification, noise filtering, compactness, and autonomy. However, these devices still have several limitations. In particular, in certain sound environments, the separation between the useful signal to be amplified and the interfering acoustic signals to be filtered remains a challenge. In this study, we propose to focus on the Cocktail Party Problem. The Cocktail Party Problem (CPP), is a psychoacoustic phenomenon that refers to the remarkable human ability to listen and selectively recognize an auditory source in a noisy environment, where the overlapping auditory interference is produced by competing speech sounds or a variety of noises that are often assumed to be independent of each other. The resolution of this type of problem, also called Auditory Attention Detection, represents a major problem for which few solutions have yet been found and which is currently the subject of intense research. This PhD thesis, which is part of the "Cyber-Physical Systems" and "Edge AI" roadmap of the Systems Department of CEA-LETI (Grenoble), will aim to make a major contribution to this Auditory Attention Detection theme, for the automatic recognition of the speaker by future generation hearing aids. The thesis will be based on advanced technological solutions using embedded artificial intelligence (Edge AI). We will address the problem through a multi-sensor data fusion approach (acoustic, inertial, video sensors). Indeed, we will consider coupling a processing of acoustic voice signals thanks to high performance microphones with a video processing of faces to realize a vocal activity detection of the speaker (automatic lip reading). The sensor data will be processed and coupled by adapted artificial intelligence algorithms. It is also envisaged to use several microphones to perform acoustic beamforming processing, and to possibly hybridize with inertial sensors to reinforce the localization estimation of the speaker. The validation of the implemented methods and the developed algorithms will be realized thanks to test campaigns in instrumented acoustic chamber (high performance microphone, video captures, etc...). Keywords: hearing aid, audio signal processing, artificial intelligence, sensor fusion, cocktail party problem, auditory attention detection

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Study and application of a FBG-based neutron/gamma dosimetry in severe radiative environment, extension to monitoring within the RJH experimental reactor

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Fibres Optiques

01-10-2021

SL-DRT-21-0904

sylvain.magne@cea.fr

Factory of the future incl. robotics and non destructive testing (.pdf)

In-core and ex-core temperature and fluence cartographies are essential data for online monitoring of operational/incidental conditions of power and experimental reactors. High fluence level and gradients combined with lack of available space and access strongly limit the deployment of in-core sensing devices. The phenomena of Radiation-Induced Attenuation (RIA) in phosphosilicate fibers is already applied to distributed dosimetry (using FBG or OFDR monitoring techniques) at temperature lower than 70°C and dose lower than some kGy. For higher temperatures (e.g. 300°C, PWR), the RIA strongly depends on both time and temperature through the recovery process. Therefore, in-core RIA-based dosimetry is still challenging and no proof-of-concept is yet established. Industry already makes large use of Fibre Bragg Gratings (FBG, wavelength-multiplexed sensors) for temperature sensing. FBGs have also great potential for dose/fluence monitoring considering the Bragg Wavelength Shift (BWS) related to the radiation-induced change of the mean refractive index of silica. The work plan for the doctoral period will consist in designing a distributed gamma/neutron dosimeter based on a dedicated multicore fibre and testing it in several radiative environments. Reliable temperature compensation will be achieved by photowriting several FBGs in each core along the same fibre section, even in presence of high dose gradients. Furthermore, each core would exhibit complementary radiosensitivity in order to discriminate several radiation contributions. This photo-inscription scheme will be reproduced at several locations along the fibre to achieve a distributed dosimetry. This design will provide a distributed temperature monitoring as well, corrected for radiative influence. The candidate will work in collaboration with laboratory engineers in charge of femtosecond laser FBG photowriting. He will be supported by two scientific collaborations between CEA and two French universities, first the PhLAM lab. (University of Lille) dealing with special preform manufacturing, fiber drawing and characterization, and second the LabHC (University of Saint-Etienne), expert in radiative phenomena in fibres. Finally, this study is supported by the INSNU project of the CEA (DES/DPE/GEN23) that provides the technical framework and access to radiative facilities, partly through agreements with foreign institutes. The LDCI lab of the CEA (DES/IRESNE/DER/SPESI) will also participate into irradiation setups. Radiation tests on X-ray generator (LabHC) and within reactors (CEA/CABRI, JSI/TRIGA, SCK/BR2) are also planned.

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Development of analysis tools based on machine learning for gamma spectrometry dedicated to in-situ measurements

Département Métrologie Instrumentation et Information (LIST)

Laboratoire de Métrologie de l'Activité

01-09-2021

SL-DRT-21-0911

christophe.bobin@cea.fr

Artificial intelligence & Data intelligence (.pdf)

Gamma-spectrum analysis is a classical technique applied for identification and quantification assessment of radionuclides in radioactive sources. Automatic identification for in-situ applications by non-expert users needs specific algorithms to meet the demand in various fields from environmental measurements to decommissioning of nuclear facilities. The Laboratoire national Henri Becquerel (LNHB), located at CEA/Saclay, is involved in the development of an automatic analysis tool of gamma spectra at low statistics based on spectral unmixing, that can be applied to scintillation detectors (NaI:Tl, plastic). The robustness of decision-making is generally limited by the variability of in-situ measurement conditions because of the deformation of gamma spectra due to attenuation and scattering phenomena in the surrounding of a radioactive source. The objective of the thesis is the development of new spectral unmixing methods based on machine learning allowing an automatic processing of these deformations of gamma spectra. The purpose is also to obtain an algorithm that can be implemented in embedded electronic systems of portable detection devices.

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Development of biosensors for early detection of pest insects using pheromone receptor-based olfactory sensors

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Diamants

01-10-2021

SL-DRT-21-0912

emmanuel.scorsone@cea.fr

Health and environment technologies, medical devices (.pdf)

This work will be carried out within the framework of the Priority Research Program "Cultivate and Protect Otherwise" project PPRCPA-PheroSensor (Early detection of insect pests using olfactory sensors using pheromonal receptors). This project, which will start in April 2021 for a duration of 5 years, is led by INRAE-UMR 1392 iEES in collaboration with INRAE-UR 1404 MaiAGE, CNRS-LORIA, ESIEE-Paris Université Gustave Eiffel, EGCE?IRD and CEA-LIST. Insects (in)directly destroy 1/3 of the world's annual harvests. Climate change and increased trade make the early detection of invasive insect pests a major challenge for optimal action before infestation. Insects use specific pheromones to attract congeners of the other sex (sex pheromones, e.g. moths) or both sexes (aggregation pheromones, e.g. weevils). These compounds are used to lure insects into traps, with the number of captures indicating population levels. This monitoring method has drawbacks: it requires frequent human intervention (counting / identification of catches) and an attractive pheromone diffusion, which is sometimes difficult to maintain. Detecting insect pheromones is an alternative for insect monitoring, but a challenge due to the low amounts emitted. PheroSensor will go beyond the most advanced odor detection technologies by developing innovative bio-inspired sensors to detect harmful insects.

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Reliability improvement of photovoltaic modules with coupled modelling / experimental approaches

Département des Technologies Solaires (LITEN)

Laboratoire des applications modules

01-09-2021

SL-DRT-21-0913

bertrand.chambion@cea.fr

Solar energy for energy transition (.pdf)

The performance and lifespan of photovoltaic (PV) modules depend on their ability to withstand various environmental constraints, including those related to thermomechanical phenomena. While this observation is true for ?classic? modules packaging installed in PV power plants, it is amplified for installation in specific climates areas, and becomes dimensioning and critical for the development of innovative modules for applications in severe environments. . The objective of the PhD work is to improve the reliability and lifespan of photovoltaic modules through thermomechanical simulation, by anticipating the behaviour of internal stress levels during the life of the PV modules. For a given application, this understanding will allow adjustments of the initial thermomechanical state within a module after assembly, in order to optimize its lifespan. The work is planned to be devided into the following steps: - First, state of the art on PV technologies, module assembly processes and understanding the existing works within the teams, particularly on the thermomechanical numerical models linked to the PV modules assembly (initial at t0). Also, specific ageing protocols will be developed on key materials (focus on polymers), associated to material characterization tools, to determine the thermomechanical material characteristics variations during aging. - Secondly, a thermomechanical model will be built, verified by experimental tests and will allow to integrate the variation of material properties during the life of the PV module. - Finally, the scientific perspective on these two first phases will result in a predictive numerical tool able to set the optimal thermomechanical state of a module after assembly (t0), to optimize the module lifespan for a given application.

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Mmwave OTA testing based on field synthesis for 5G/6G systems

Département Systèmes (LETI)

Laboratoire Antennes, Propagation, Couplage Inductif

01-09-2021

SL-DRT-21-0942

raffaele.derrico@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

This PhD program is proposed within the framework of CEA-LETI's R&D activities in the field of wireless transmission technologies and 5G / 6G systems using millimeter waves. In these networks a large number of antennas will be used in order to increase the datarate and be able to serve a large number of users. The radio system performance will depend on both R&D choices and deployment conditions. The objective of this thesis is to propose Over-the-Air (OTA) test methodologies in a controlled environment, which makes it possible to reproduce realistic propagation conditions and thus be able to evaluate the performance of future communications systems, without carrying out long environmental measurement campaigns. real. To this purpose, a methodology based on fading emulator combined with intelligent surfaces is considered to reproduce the multi-path channel. The study will begin with a state-of-the-art model of the 5G propagation channel and OTA methodologies. Then the PhD student will propose a theoretical modeling of the testbed based on an analysis of spherical modes and an optimization for planar wave synthesis. Then an experimental implementation of the proposed methodology will be realized. The PhD student will be part of the Antenna, Propagation and Inductive Coupling Laboratory at CEA-LETI, in Grenoble (France). He/she will benefit of the state of the art facilities (channel sounders, emulator, OTA setup, and electromagnetic simulator). Application: The position is open to outstanding students with Master of Science, ?école d'ingénieur? or equivalent. The student should have specialization in the field of telecommunications, radar, microwave and/or signal processing. The application must necessarily include a CV, cover letter and grades for the last two years of study.

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A study of diffusion welding of alloy 800 and its application to compact heat exchangers

Département Thermique Conversion et Hydrogène (LITEN)

Laboratoire Conception et Assemblages

01-10-2021

SL-DRT-21-0950

emmanuel.rigal@cea.fr

Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Alloy 800 is a highly corrosion resistant steel suitable to manufacture steam generators (SG). Diffusion welding is a solid state joining process carried out through the application of high pressure and temperature to materials. When applied to grooved plates, it allows to obtain highly compact heat exchangers and this technological solution is envisaged for the fabrication of the SG of Small Modular nuclear Reactors. However, diffusion welding of alloy 800 is difficult because a profuse precipitation of carbides and oxides at interfaces during welding. First, the PhD subject consists in studying this phenomenon for different initial condition of the material and, more generally, in studying how the material evolves during a high temperature exposure. Conditions favourable to diffusion welding will be defined. Then, the diffusion welding process will be simulated using models previously developped at the laboratory and/or within partner academic labs. The models will need to be completed. Those two first items will allow to define suitable diffusion welding conditions that will be used for the fabrication of joints. The latter will be characterised, both from the microstructure and the mechanical properties point of view. The link between the initial microstructure, the process parameters, the final microstructure and the porperties will be studied.

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New overlay techniques for advanced technologies measurement

Département des Plateformes Technologiques (LETI)

Laboratoire Microscopie Mesures et Défectivité

01-09-2021

SL-DRT-21-0956

yoann.blancquaert@cea.fr

Advanced nano characterization (.pdf)

Overlay (OVL) is one of the key parameters to be monitored during the microelectronic components fabrications. Currently, this parameter is monitored by imaging techniques or by scatterometry. For the most advanced technologies - CMOS10nm and beyond - these techniques, although accurate (<0.4nm in 3 sigma), have difficulty to meet the needs of the process. Other techniques need to be evaluated by simulations and experimentally to achieve lower accuracies. CD-SAXS and CD-SEM are the two techniques that need to be evaluated for this ultimate metrology. The accuracy of current techniques will be evaluated, new measurement methodologies will be defined and inter-technique reference standards will be created. This subject is in the continuity of ongoing European collaborations and programs.

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Distributed intrusion detection in a constrained edge network context

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Systèmes Communiquants

01-09-2021

SL-DRT-21-0965

alexis.olivereau@cea.fr

Cyber security : hardware and sofware (.pdf)

The objective of the proposed PhD thesis is to define a distributed, autonomous, and reactive security system able to reconfigure itself in real time in order to take into account in particular the possible attacks, the traffic pattern to be monitored and its own resources. In this system, intrusion detection probes are essential components. They implement anomaly-based detection using artificial intelligence, capable of detecting weak signals in a potentially very high-speed environment. The analyses of the different probes are correlated in order to increase the overall capacity to identify malicious behavior on the scale of the network to be protected. Finally, the question of energy efficiency is to be considered both at the level of individual probes and that of the orchestration of the global monitoring function. PhD thesis subject proposed in the framework of the European GREENEDGE project: https://greenedge-itn.eu/wp-content/uploads/2021/03/ESR11_description.pdf All applications must be submitted via the GREENEDGE project website: https://greenedge-itn.eu/phd-hiring-call/

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Interconnection reducing or removing the use of non sustainable material to reduce photovoltaic footprint

Département des Technologies Solaires (LITEN)

Laboratoire des applications modules

01-10-2021

SL-DRT-21-0966

vincent.barth@cea.fr

Solar energy for energy transition (.pdf)

Photovoltaic (PV) industry consumes nearly 10% of the world's silver production (2019 data) and its constant growth is likely to create a short-term problem. Silver is mainly used in the interconnection of photovoltaic cells, specifically for the metallization. Today heterojunction cells provide the highest efficiency and a low temperature coefficient. These characteristics allow to obtain an excellent LCOE (Levellized Cost Of Energy). However, mandatory use of low temperature silver paste which are less conductive and cannot be easily soldered requires to deposit more silver and the problem of silver consumption is further amplified. Different approaches can be explored to solve this problem. Increasing wires number, cutting the cells or using low temperature soldering methods are the first solutions. Replacement of silver by other conductive particles are another way. The thesis proposes to explore the different approaches and after an LCA analysis to develop the most interesting one for a sustainable development of PV.

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Development of two-dimensional (2D) GaSe / InSe heterostructures for the realization of new concepts of nano- and opto-electronic devices that save on critical elements III

Département des Plateformes Technologiques (LETI)

Laboratoire des Matériaux pour la photonique

01-11-2021

SL-DRT-21-0973

berangere.hyot@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

With the very strong growth in the number of connected objects, which should almost triple, from 8.74 billion in 2020 to 25.4 billion in 2030, access to raw materials is becoming a major economic and geopolitical issue for the field of nanoelectronics. In order to develop sustainable nanoelectronics, it becomes necessary to design and produce devices (memories, optoelectronic components, etc.) by reducing or substituting their critical constituent elements. The notion of criticism can cover many aspects; a raw material can be classified as critical because of the available world reserves, production monopolies and possible associated geopolitical conflicts, the economic weight of the material on the balance of one or more countries or even ethical reasons (working conditions or environmental impact of extraction conditions). For example, many light emitting and light sensors devices (laser sources, light emitting diodes, photodetectors / imagers, etc.) are based on III-V semiconductors (alloys composed of elements from columns III and V of the periodic table). However, some of these elements, such as gallium (Ga) and indium (In), are now considered critical by the European Commission and it is urgent to reduce their consumption. Two-dimensional (2D) or lamellar III-VI (GaSe and InSe) semiconductors made up of only a few atomic monolayers now appear very promising for designing new architectures of optical sensors or memory cells that save on III elements. Compared to a conventional III-V component, a gain of 5 orders of magnitude can be expected on the consumption of Ga and In. Today, most devices based on III-VI materials presented in the literature are prepared by mechanical exfoliation of bulk crystals of GaSe or InSe, a technique allowing to carry out proofs of concept (the typical average dimension of exfoliated crystals is a few µm) but incompatible with the large scale and low cost manufacture of these components. The objective of the thesis is to work on the development of MOCVD growth of III-VI materials and their heterostructures directly on large-dimension silicon substrates (diameter = 300 mm). It should be noted that the combination of this growth technique with the use of silicon substrates makes the results of this work directly compatible with the production tools of microelectronics. The structural properties of these heterostructures will be investigated by X-ray diffraction (XRD), atomic force microscopy (AFM), transmission electron microscopy (TEM) and Raman spectroscopy. Their physical properties (electronic or optoelectronic) will be explored through the production of suitable devices. A previous study made it possible to overcome a certain number of difficulties linked to the integration of these materials and to implement various technological bricks (lithography, identification of compatible chemistries?) for the realization of functional devices. We will focus in this new thesis on the realization of two types of devices: optoelectronic devices (mainly photodetectors) and memory cells (or memristors). In both cases, we plan to work on vertical heterostructures where the properties of the silicon substrate will be taken advantage of. The expectations of these devices are wavelength selectivity adjustable from UV to IR and good conversion efficiency for photodetectors and a low operating voltage for memory cells allowing low-power electronics applications (neural networks for example). A theoretical approach involving ab initio calculations of GaSe / InSe superlattices with determination of the electronic and optical properties of different structures will support the experimental development described above and may serve as a guide in the design of new functional heterostructures (whether dedicated to photodetection or resistive memories). The candidate, depending on his/her sensitivity, can either get involved in the development of these mathematical models or participate in their analysis with the collaborators in charge of the study.

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Synthesis and photophysical characterization of new cyclophane molecules

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-09-2021

SL-DRT-21-0991

guillaume.bertrand@cea.fr

During our recent developments in the field of radiation detection for nuclear instrumentation, we showed a benefic effect of cyclophane doping in plastic scintillators for neutron/gamma discrimination. The goal of this thesis is to perform a systematic study, both theoretical and experimental; of this phenomenon by synthesizing a library of polyaromatic cyclophane molecules. These molecules are for most of them new and will form the ground for the future theoretical and applicative exploration. Hence, a strong background in organic chemistry is required for this PhD. The other ambition of this thesis is to build a theoretical framework for the numerous photophysical processes that can occur in cyclophane molecules, with a very peculiar structure. This work will be based on a collaboration between three laboratories: CEA Saclay, for the nuclear detection application (DRT/LCAE); ENS Paris-Saclay, for photophysics research (PPSM); and Université de Versailles-Saint-Quentin, for organic chemistry (ILV/SORG).

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Diffuse Reflectance Spectroscopy using an array of Germanium photodiodes

Département d'Optronique (LETI)

Laboratoire d'Imagerie thermique et THz

01-09-2021

SL-DRT-21-1008

luc.andre@cea.fr

Photonics, Imaging and displays (.pdf)

With the increasing demand for autonomous vehicles, LETI-DOPT has developed a large portfolio of state of the art optical components including low cost focal plane sensitive in the SWIR range [900-1600nm]. In parallel, LETI-DTBS gathered a strong expertise in Diffuse Reflectance Spectrometry to measure the concentration of various chromophore in biological tissue. Formerly tuned in the visible range, this technique could be shifted in the SWIR range to detect water, oil or sugar, and benefit from the technological effort for optical components and finally propose low cost and robust biosensors. As the worldwide diabetics population is today estimated larger than 400 Million people, the targeted biosensors would represent a major breakthrough from an industrial and societal point of view. In this perspective, the robustness is a strong plus. In order to qualify then to improve it, we propose a two-pronged approach. In a first time, the candidate will work on an available lab setup made from fibered components from the shelf: black body source, spectrometer featuring an InGaAs cooled camera, and post-processing based on a Monte-Carlo algorithm. The Device Under Test is synthetic, homogenous and its optical properties are well-known. The goal is to qualify the setup: a strong simulation part of scattering medium is expected. In a second time, a design of the focal plane is expected in close collaboration with the team in charge of the realization. Last, the focal plane could be bonded on a read-out circuit to be embedded on a portable prototype. The overall response will be qualified in more complex heterogenous media, to finally asses the biosensor in realistic biological tissue.

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