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

PhD : selection by topics

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

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des système embarqués et autonomes

01-01-2019

SL-DRT-19-0401

shuai.li@cea.fr

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.

Tunable Metasurface

Département Systèmes

Laboratoire Antennes, Propagation, Couplage Inductif

01-06-2019

SL-DRT-19-0406

jean-francois.pintos@cea.fr

Metamaterials have been studied by the scientific community for several years with a particular focus on 2D or 3D meta shapes. In the antenna field, these structured materials have been mainly used as magnetic surfaces, filtering surfaces for surface waves or the antenna itself. The main disadvantage of these materials is its narrow band behaviour. Recent research has shown that it is possible to modify the response of metasurfaces by adding a film sensitive to a control voltage to the patterns or by arranging the active components between them. More recently, CEA Leti has developed a new approach, through a thesis, to modify the performance of a metasurface, by inserting control devices on its rear surface as well as on the feeder. The proposal, made here, is in line with the continuity of this work, initiated within the LAPCI laboratory, with a specific development around massively tunable metasurfaces. Indeed, it has been demonstrated that the metasurface/feeder pair should be jointly designed/optimized when the metasurface and/or feeder were compact or even miniature. The purpose of this thesis is to study this interaction through the notion of load impedance and to realize a final demonstrator of a reconfigurable metasurface of several hundred active elements. The main interest is to consider the use of ultra-compact adjustable metamaterials in order to miniaturize the size of an antenna placed near a reflecting plane. The second major point concerns the possibility of frequency-dependent control of the complete device (by nature very narrow band) over a frequency band of several tens of percent. During this thesis, the candidate will develop the theoretical modeling of the proposed device and validate the expected performances through 2D and/or 3D electromagnetic simulation campaigns. He/she will be in charge of having the selected demonstrators carried out and will carry out measurements of the devices in the test facilities of CEA-Leti and/or CNES (anechoic chamber). The candidate will be integrated into the Antenna, Propagation and Inductive Coupling Laboratory in Grenoble. He/she will be part of the research team (permanent, doctoral and non-permanent) and will be supervised by a research engineer from the laboratory. The candidate will be required to present his or her work at national and international conferences and symposiums.

Miniature and directive antenna design with frequency agility over several octaves

Département Systèmes

Laboratoire Antennes, Propagation, Couplage Inductif

01-09-2019

SL-DRT-19-0423

serge.bories@cea.fr

The 'New Space' sector pushes for innovative solutions concerning on board micro-satellites antenna design. With smaller satellites, the miniaturization of directive and extremely wide band antenna represents a solution to fill the requirements of a lot of services. The double circular polarization needs to be ensure properly over more than 2 octaves. The CEA Leti antenna laboratory proposes to skirt the classical antenna physical limitation (bandwidth / miniaturization) by tuning the antenna on a smaller instant sub-band that can be shifted with reconfigurable RF components. This is the concept of antenna aperture tuning. The novelty of the PhD subject is to extend the tuning range over several octaves thanks to tunable capacitors developed at CEA Leti. The challenge consists to optimize the miniaturization of the antenna structure while limiting the impact of losses introduced by the tunable capacitors and get a performance stability over several octaves. Prototypes will be realized and measured in the CEA Leti or CNES anechoic chamber.

Study of new solutions for the security of embedded systems

Département Systèmes

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-09-2019

SL-DRT-19-0426

pierre-henri.thevenon@cea.fr

In recent years, the number of connected systems has increased exponentially and is expected to reach several tens of billions by 2020. Most of these devices integrate seldom, if ever, security and can create massive attacks involving a large number of objects. In the embedded systems used in IOT and I-IOT, hardware and software solutions currently exist and provide cryptographic primitives to secure a communication interface or data storage. However, these solutions are not always correctly implemented and didn't deal with all the issues of security. Based on the study of existing attack scenarios, standards and regulatory documents, this thesis will define the needs in terms of security of an embedded system throughout its life cycle. Particular attention should be paid to threat detection, hardware and software integrity, system resilience, and the definition of a new commissioning interface. New solutions will be studied and developed in order to address issues not integrated in current embedded devices. The implementation of these new solutions will be the first step in the development of a new component called a security supervisor. One day, this component could be integrated in most of embedded systems in order to strengthen defence in depth.

Flexible nanosensors matrix for impact detection on sensitive surface

Département Systèmes

Laboratoire Autonomie et Intégration des Capteurs

01-09-2019

SL-DRT-19-0434

elise.saoutieff@cea.fr

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.

Adaptative frequency tuning electronic systems for broadband vibration energy harvesting

Département Systèmes

Laboratoire Autonomie et Intégration des Capteurs

01-10-2019

SL-DRT-19-0436

pierre.gasnier@cea.fr

Energy harvesting is a theme whose goal is to supply power to communicating Wireless Sensor Nodes (WSN) by replacing their electrical power source (battery, cables) or by increasing their energy autonomy. Vibration energy harvesting, in particular, makes it possible to exploit the mechanical energy of an environment and convert it into electricity in order to supply the WSN. The proposed PhD thesis will focus on the use of the piezoelectric transduction to convert vibration energy into electricity. One of the major drawbacks of these harvesters is their frequency selectivity: the use of mechanical resonators amplifies ambient vibrations, but the harvested power drastically drops when the harvester and the environment are no longer tuned in frequency, which degrades the operability of the system and therefore its reliability. For the adoption of this type of system by industry, one of the main major barriers is therefore this frequency selectivity. This can be solved by means of so-called "broadband" harvesters and/or with the ability to be dynamically tuned by an electronic system. Indeed, coupled to an intelligent electronics, a "strongly coupled" harvester can see its mechanical behaviour modified (change in its stiffness for example) which makes it possible to 1) follow the evolution of the input frequency (a motor whose rotation frequency slows down, ...) and/or 2) compensate for its own intrinsic properties (its resonance frequency that decreases with temperature, ageing...). The core of the proposed work therefore focuses on electronics and power management circuits that adapt the mechanical behaviour of such harvesters according to the input vibration. The CEA and the Savoie Mont-Blanc University (SYMME Laboratory) have already proposed high-performance techniques to carry out this frequency tuning. However, the automatic adjustment part of these circuits has not been investigated. The objective of the thesis is to propose, dimension, simulate, realize and test innovative electronic architectures allowing automatic tuning and maximum power point tracking of a piezoelectric harvester. After a state of the art study on frequency adjustment means and techniques, a system study and electromechanical simulations will have to be carried out, which will make it possible to select the relevant implementations (Full analog, or mixed digital-analog). Particular care will be taken to ensure that the proposed circuit is low power and takes up little space, since the ultimate goal is to build a circuit that is autonomous in terms of energy and consumes a negligible proportion of the harvested electrical energy. At the end of the Phd work, the selected architecture(s) will then be proposed to the integrated circuit design department for miniaturization. A complete demonstrator (harvester + tuning technique + adjustment circuit) is targeted at the end of this thesis.

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