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

PhD : selection by topics

Fabrication and characterization of normally-off GaN high electron mobility transistor (HEMT)

Département Composants Silicium (LETI)




GaN-HEMT silicon substrate technologies are seriously considered as the next generation of power electronics devices between 200V and 1200V. The basic component for this new technology is the Normally-Off transistor. The latter must have electrical figures of merit perfectly mastered and reliable over time (voltage-breakdown, threshold, leakage current grid, saturation current ....). A promising approach for "normally-off" operation is to use a metal-insulator-semiconductor architecture for HEMT (MIS-HEMT). However, the expected performance for the reliability of these components subjected to high voltage, current and temperature constraints require a material quality with less defects and a perfect technological mastery. Two key steps of this technological process are the etching of the cap on the active area of ??the transistors and the deposition of the gate dielectric and its associated surface pretreatment. These steps directly control the instabilities of the threshold voltage of the transistors related to the interface states, gate leaks and therefore to the proper operation of the latter. For GaN MIS-HEMT technologies to be widely adopted in the power electronics devices of the future, it is necessary to be able to remove the technological barriers related to the manufacture of these devices. The objectives of the phD research project proposed jointly by the LN2 in Sherbrooke, Quebec, Canada and the LTM in Grenoble, France is then to fabricate a normally off GaN MIS-HEMT with optimal electronic performances

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Towards a logics and tooled framework for the refinement and verification of properties that improve privacy and data protection in systems

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire exigences et conformité des systèmes



The overall objective of this PhD is to define, specify and deploy a formal framework (logics) which allow to (1) model systems including data processes, and (2) verify properties related to the Privacy and Data Protection (PDP) area. To do so, the candidate should first define a formal language supporting systems modeling in which data flows, users/stakeholders, processing units and storages interact. The language should be enriched with a formal semantics (for instance an operational semantics) in order to support the verification of high-level requirements derived from privacy-related-risks methods like LINDDUN (, Linkability, Identifiability, Non-repudiation, Detectability, Disclosure of information, Unawareness, Non-compliance). Once the framework is already defined, a set of algorithms should be designed and deployed in order to verify the properties and relations associated to the high-level privacy requirements. To accomplish this phase, the candidate should set appart properties to be verified at model level from those to be verified at code level (for instance, targetting a C-code). A refinement method and the application of existing verification tools (concretely the Frama-C tool, are foreseen. In addition, the high-level requirements and systems modeling module will be deployed on the top of the Papyrus environment (

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Inverse reinforcement learning of a task performed by a human




Learning from demonstration involves an agent (e.g., a robot) learning a task by watching another agent (e.g., a human) performing the same task. It often uses reinforcement-learning methods to improve the robot's ability to perform a task in new situations (i.e., generalization). These methods involve providing a positive reinforcement (i.e., a reward) when the outputs of the algorithms help achieving the task, but require a human designed reward function. The more the task is complex the more difficult is the reward function to design, but it can be learned from a series of examples with methods called inverse reinforcement learning. The use, jointly or not, of these techniques has shown encouraging results, but which are limited to toy examples and cannot be adapted as such to tasks more representative of the industrial environment. During the thesis, the PhD student will analyze and test state-of-the-art previous works. S/He will then propose a method, combining inverse reinforcement learning to other algorithms (e.g., generative adversarial networks, GAN), so that the robot will understand the task performed by the operator (with as little explanation from the operator as possible), and will generalize enough to make the robot robust to dynamic environments (obstacles, moving objects?). This method should be suited for a "pick and place" task in an industrial environment and ensure a reasonable enough learning period (information a priori, feedback from the operator) for tasks of medium complexity.

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Understanding TCO/a-Si:H interface limitations in heterojunction solar cells: improvement in front and back side for bifacial devices

Département des Technologies Solaires (LITEN)

Laboratoire HETerojonction



In this work, the interface between the transparent conductive oxide (TCO) layer and the hydrogenated amorphous silicon (a-Si:H) layer of silicon heterojunction (SHJ) solar cells will be extensively studied for the front and the back sides of bifacial devices. The main objectives will be: 1- Characterize the interface by means of electrical and morphological characterization for different amorphous layers and different TCO materials or TCOs deposited by different techniques (PVD, ALD, SALD, others). Electrical simulation can be also performed. 2- Optimize the interface according to the results of characterization and simulation by tuning the interfacial properties of the a-Si:H and TCO layers and integrate the optimized layers into SHJ solar cell devices. Optical and electrical characterization of the final devices will also be performed. 3- Define the best configuration of the a-Si:H/TCO stack for bifacial solar cells in order to increase the bifaciality of the devices.

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Towards a tooled framework and method for safety and security co-engineering of Ciber-Physical systems guided by the integration, refinement and verification of patterns

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire exigences et conformité des systèmes



Problematics and main goal: Nowadays, the so called Cyber-Physical Systems (CPS) are deployed in a variety of application domains like automotive, aeronautics, health care, etc. The impacts in case of failures or misbehaviours, due to accidental faults or attacks, may be critical with respect to economical, business, and safety criteria and, in the end, potentially jeopardize human lives. According to the principle called correct-by-design, an effective identification and management of safety and security risks is crucial and should be conducted at early design phases in the systems development cycle. However, the state of the art of approaches for safety and security engineering shows that, for many cases, the analyses are conducted independently and, more importantly, without including a co-engineering phase that ensures their consistency. Despite that, several known CPS case studies (e.g.,in the automotive domain) exhibit a clear and critical entanglement between safety constraints (e.g., performance and latency) and security exigencies (e.g., ciphering mechanisms). To prevent potential conflicts and ensure consistency between safety and security exigencies, a joint safety-security analysis need to be conducted. It is clear that a joint safety-security analysis may not be necessary for certain systems, however for those that need it, the co-engineering phase can become critical. Since safety and security analyses can be conducted independently and are indeed challenging subjects due to their inherent complexities, new methods, formal languages, techniques and tools are needed to better support and ease safety-security co-engineering. The proposed Ph.D. targets a formal framework and tool to integrate this decisive phase into the systems development cycle.

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Machine Learning for a precision agriculture




This PhD is at the interface between agriculture and Machine Learning. The project is based on a collaboration between the CLAAS company based in Woippy (Moselle) specialized in the manufacture of "high-end" agricultural Equipment and CEA Tech in Metz. This PhD is part of the statistical modeling of an agricultural press system. It aims to design a parametric statistical model by supervised learning, to automate the compression procedure of the biological material and help the farmer in this task. This model must meet physical and environmental constraints.

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