Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Technological challenges >> Smart Energy grids
19 proposition(s).

See all positions [+]

Safety/Security Modeling for Security Characterization of Industrial Control Systems

Département Systèmes (LETI)

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-10-2021

SL-DRT-21-0031

Cyber security : hardware and sofware (.pdf)

Industrial systems are often used to monitor and control a physical process such as energy production and distribution, water cleaning or transport systems. They are often simply called Supervisory Control And Data Acquisition (SCADA) systems. Due to their interaction with the real world, the safety of these systems is critical and any incident can potentially harm humans and the environment. Since the Stuxnet worm in 2010, such systems increasingly face cyberattacks caused by various intruders, including terrorists or enemy governments [1]. As the frequency of such attacks is increasing, the security of SCADA systems becomes a priority for governmental agencies [2]. One of the main research axis in cybersecurity of industrial systems deals with combination of safety and security properties. Safety relates to applicative properties of the system (e.g. chemical properties for a chemical factory); while security properties take into account how an intruder can harm the system. As show in [3], combining safety and security is a challenging topic as these properties can be either dependent, strengthening, antagonist or independent. As show in [4], combining both safety and security in a common modeling is challenging as both come with sources of combinatorial explosion. Moreover, there are tools used either for security or safety analyzes but currently no tool is able to handle both aspects at the same time. In this context, we propose a Ph.D thesis revolving around modeling of industrial systems taking into account both safety properties of the physical process and security properties. Besides the definition of an accurate, yet automatically analyzable modeling framework/language, many aspects can be part of the subject. For instance, programmable automata (PLC) configuration files could be generated from this model in order to only deploy programs validated beforehand. PLC vulnerabilities could be studied (firmware reverse engineering, protocol fuzzing) in order to test the technical feasibility of found attacks. Finally, in a certification context, security analyzes on the model could include requirements from standards such as IEC 62443 [5] to help evaluation process. Références [1] J. Weiss, Protecting industrial control systems from electronic, Momentum Press, 2010. [2] ANSSI, Managing cybersecurity for ICS, ANSSI, 2012. [3] L. Piètre-Cambacédès, Des relations entre sûreté et sécurité, Paris: Télécom ParisTech, 2010. [4] M. P. a. A. K. M. Puys, Generation of applicative attacks scenarios against industrial systems, Nancy: FPS'17, 2017. [5] IEC-62443, Industrial communication networks - Network and, International Electrotechnical Commission, 2010.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

DC/DC converter based on piezoelectric material

Département Systèmes (LETI)

Laboratoire Electronique Energie et Puissance

01-10-2020

SL-DRT-21-0277

ghislain.despesse@cea.fr

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

The aim of this thesis is to design high-efficiency power converters based on resonating piezoelectric transducers. A large part of the work is to develop the electrical cycle able to energetically maintain the piezoelectric resonator in resonance and ensure zero-voltage switching, for electrical energy transfer from the source to the piezoelectric resonator or from the piezoelectric resonator to the output, in order to minimize the losses. An electronic power management circuit will be designed to enable this ideal energetic cycle. This electronic circuit will include several regulation loops to ensure the system stability and regulate the electrical output power. Finally, a study of the piezoelectric transducer size reduction will be done in view of a MEMS (Micro Electro Mechanical System) integration.

Download the offer (.zip)

Robustness and performances of improved electrodes for solid oxide cells application

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire Production d'Hydrogène

01-10-2021

SL-DRT-21-0289

maxime.hubert@cea.fr

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

Solid oxide cells (SOCs) are electrochemical devices operating at high temperature that can directly convert fuel into electricity (fuel cell mode ? SOFC) or electricity into fuel (electrolysis mode ? SOEC). In recent years, the interest on SOCs has grown significantly thanks to their wide range of technological applications that could offer innovative solutions for the transition toward a renewable energy market. However, despite of all their advantages, the SOCs lifetime is still insufficient to envisage the industrial deployment of this technology. Indeed, the SOCs durability remains limited by various degradation phenomena including a mechanical damage in the electrodes. For instance, the formation of micro-cracks in the so-called ?hydrogen' electrode is a major source of degradation. However, the precise mechanism and the full impact of the micro-cracks on the electrode performances are still unknown. By a multi-physic modelling approach, it is proposed in this thesis to establish the link between the loss of performances and the mechanical damage in the hydrogen electrode. Once the model validated on dedicated experiments, a sensitivity analysis will be conducted to provide relevant guidelines for the manufacturing of improved robust and performant electrodes. One or two solutions will be selected and evaluated after manufacturing for final validation.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

Optomechanical reference oscillators

Département Composants Silicium (LETI)

Laboratoire Composants Micro-Capteurs

01-09-2021

SL-DRT-21-0351

marc.sansaperna@cea.fr

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

Reference ocillators (clocks) are devices that generate signals with a very precise frequency, generally defined by the vibration of a mechanical element at resonance. Nowadays, they are ubiquitous elements in electronic circuits: for example, a smartphone or tablet can contain up to seven reference oscillators. However, the appearance of new technologies such as 5G or autonomous vehicles requires a level of performance that is not attainable by commercial clock technologies. One of the most promising routes to improve performance is the development of clocks based on micro-electromechanical (MEMS) resonators at high frequency (1-5 GHz, tens of GHz in the future). However, building high-performance MEMS resonators in the GHz range is highly challenging, mainly due to the difficulty of detecting their minuscule vibration amplitudes. One promising solution is to use optomechanical detection, using the same principle as gravitational wave detectors but integrated at nanometric scale. This technology, now well mastered at Leti, can be combined with the integration with piezoelectric materials to increase the attainable signal levels. This principle, recently demonstrated by several fundamental research groups, is now mature enough to evolve towards applications, and solves many of the difficulties involved in the implementation of MEMS clocks in the GHz range. The objective of the thesis is to develop a MEMS clock based on this novel optomechanical technology. The thesis will take place in the Microsensors Laboratory of the CEA-Leti, in collaboration with the RF Components Laboratory. The Leti is a pioneer in the implementation of on-chip optomechanical and piezoelectric resonators. The PhD student will work in collaboration with Leti researchers to design the MEMS resonators and their fabrication process, based on an analytical study and finite-element simulations. Then, the student will have the opportunity to contribute to the fabrication of the devices in clean room. Finally, the student will characterize them in the Leti's laboratories, to extract their performance and implement a first demonstrator of optomechanical MEMS clock. The candidate will have a M.Sc. or equivalent degree, with a formation as a generalist engineer or physics, and a specialization on semi-conductor physics, nanotechnology, optics or a closely related field.

Download the offer (.zip)

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.

Download the offer (.zip)

Improving Side-channel based instruction disassembling

Département Systèmes (LETI)

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-09-2021

SL-DRT-21-0375

thomas.hiscock@cea.fr

Cyber security : hardware and sofware (.pdf)

Side-channel based disassembling is a family of side-channel attacks that strive to recover instructions executed by a processor from some of its physical emanations. Power consumption or electro-magnetic (EM) field are the most widely exploited sources of leakage. From a security designer perspective, knowing the best possible attacks is mandatory to secure a product. Le LSOSP laboratory is very active of this topic. In 2019 it proposed a new approach for performing side-channel based disassembling called mono-bit reconstruction, which was proved to be very effective on small microcontrolers. The main goal of this PhD is to study whether (or not) such attacks are possible on complex cores, such as processors found on smartphones. We will study how the complex microarchitecture of recent processors affect the leakages (Power or EM)? And how can we still extract some information from it? The latter will require to develop machine Learning Tools to recover information from very noisy measurements. At the end of the PhD, we hope to have a better understanding of the side-channel disassembling and some reflexion on countermeasures that would mitigate them.

Download the offer (.zip)

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.

Download the offer (.zip)

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.

Download the offer (.zip)

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,...).

Download the offer (.zip)

Study, evaluation, and validation of the performance of a boron measurement system using neutron absorption

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs et Architectures Electroniques

01-10-2021

SL-DRT-21-0397

adrien.sari@cea.fr

The concentration of boron in the primary circuit fluid of a nuclear reactor must be finely controlled in order to guarantee its safety. Indeed, an excursion of the boron concentration could potentially lead to a risk of criticality. An online nuclear measurement system is therefore required to monitor the boron concentration in the primary reactor fluid. Such a system is commonly referred to as a "boronmeter". The proposed thesis subject is made up of three lines of research. The first line aims to study by Monte Carlo simulation, and then conceptualise and theoretically formalise the behaviour of the two main performance criteria (counting rate and contrast) under the influence of the different characteristics of the boronmeter. The second line of research aims at evaluating and experimentally validating the interpretation of the effects brought into play within the boronmeter and the theoretical concepts formulated. This experimental work will be carried out in close collaboration with the Laboratoire National Henri Becquerel (LNHB). The third line of research of this thesis aims to design an innovative boronmeter designed to measure the boron concentration in the primary fluid as close as possible to the core of the nuclear reactor. Such a system would make it possible to identify as quickly as possible an anomaly in the boron concentration value at the reactor core. However, the constraints imposed by such a measurement environment will have to be taken into account, and a suitable measurement methodology will be developed. Different approaches to temperature and fluid flow compensation will be the subject of in-depth investigations.

Download the offer (.zip)

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.

Download the offer (.zip)

See all positions