Predictive Diagnosis and Ageing Trajectory Estimation of New Generation Batteries through Multi-modalities Fusion and Physics-Informed Machine Learning
Context:
Lithium-ion and emerging Sodium-ion batteries are crucial for energy transition and transportation electrification. Ensuring battery longevity, performance, and safety requires understanding degradation mechanisms at multiple scales.
Research Objective:
Develop innovative battery diagnostic and prognostic methodologies by leveraging multi-sensor data fusion (acoustic sensors, strain gauge sensors, thermal sensors, electrical sensors, optical sensors) and Physics-Informed Machine Learning (PIML) approaches, combining physical battery models with deep learning algorithms.
Scientific Approach:
Establish correlations between multi-physical measurements and battery degradation mechanisms
Explore hybrid PIML approaches for multi-physical data fusion
Develop learning architectures integrating physical constraints while processing heterogeneous data
Extend methodologies to emerging Na-Ion battery technologies
Methodology:
The research will utilize an extensive multi-instrumented cell database, analyzing measurement signatures and developing innovative PIML algorithms that optimize multi-sensor data fusion and validate performance using real-world data.
Expected Outcomes:
The thesis aims to provide valuable recommendations for battery system instrumentation, develop advanced diagnostic algorithms, and contribute significantly to improving the reliability and sustainability of electrochemical storage systems, with potential academic and industrial impacts.
Sub-THz programmable electromagnetic surfaces based on phase change material switches
Spatiotemporal manipulation of the near- and far-electromagnetic (EM)-field distribution and its interaction with matter in the THz spectrum (0.1-0.6 THz) is of prime importance in the development of future communication, spectroscopy, imaging, holography, and sensing systems. Reconfigurable Intelligent (Meta)Surface (RIS) is a cutting-edge hybrid analogue/digital architecture capable of shaping and controlling the THz waves at the subwavelength scale. To democratize the RIS technology, it will be crucial to reduce its energy consumption by two orders of magnitude. However, the state-of-the-art does not address the integration, scalability, wideband and high-efficiency requirements.
Based on our recent research results, the main objective of this project will be to demonstrate novel silicon-based RIS architectures s at 140 GHz and 300 GHz. The enhancement of the THz RIS performance will derive from a careful choice of the silicon technology and, from novel wideband meta-atom designs (also called unit cell or element) with integrated switches based on PCM (phase change material). The possibility of dynamically controlling the amplitude of the transmission coefficients of the meta-atoms, besides their phase, will be also investigated. Near-field illumination will be introduced to obtain an ultra-low profile. To the best of our knowledge, this constitutes a new approach for the design of high-gain antennas in the sub-THz range.
Defense of scene analysis models against adversarial attacks
In many applications, scene analysis modules such as object detection and recognition, or pose recognition, are required. Deep neural networks are nowadays among the most efficient models to perform a large number of vision tasks, sometimes simultaneously in case of multitask learning. However, it has been shown that they are vulnerable to adversarial attacks: Indeed, it is possible to add to the input data some perturbations imperceptible by the human eye which undermine the results during the inference made by the neural network. However, a guarantee of reliable results is essential for applications such as autonomous vehicles or person search for video surveillance, where security is critical. Different types of adversarial attacks and defenses have been proposed, most often for the classification problem (of images, in particular). Some works have addressed the attack of embedding optimized by metric learning, especially used for open-set tasks such as object re-identification, facial recognition or image retrieval by content. The types of attacks have multiplied: some universal, other optimized on a particular instance. The proposed defenses must deal with new threats without sacrificing too much of the initial performance of the model. Protecting input data from adversarial attacks is essential for decision systems where security vulnerabilities are critical. One way to protect this data is to develop defenses against these attacks. Therefore, the objective will be to study and propose different attacks and defenses applicable to scene analysis modules, especially those for object detection and object instance search in images.
RF Circuit Design for Zero Energy Communication
Our ambition for 6G communication is to drastically reduce the Energy in IoT. For that purpose we aim at developing an integrated circuit enabling zero Energy communication.
The objective of this PhD is to design this circuit in FD-SOI and operating in the 2.4 GHz. In this PhD, we propose to use a new design technique which is currently revolutionizing the radio-frequency design. We expect that many innovations can be carried out during this PhD by combining those two innovations.
The candidate will integrate a large design team and he will participate in collaborative project at european level. As a first step, he will analyze the system constraints to choose the best architecture and derive the specifications. Then, he will formalize mathematically the performances of the backscattering technique in order to setup a design methodology. Then he will be working full time on circuit design, sending to fabrication two circuits in 22 um technology. He will be also involve in the test of the circuit as well as in the preparation of a demonstrator of the backscattering techniques. We expect to publish several papers in high level conferences.
Study of 3D pattern etch mechanisms into inorganic layers for optoelectronic applications
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. In the same way, devices based on diffractive optic and hyperspectral sensors are looking for complex multi-height structures. Finally, recent micro-display technologies for augmented reality (AR) and virtual reality (VR) require 3D structures difficult to achieve with conventional micro-fabrication technics.
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). Once these structures achieved in photoresist, it is necessary to transfer them in an adapted functional layer using plasma etching. The etch mechanisms behind the transfer of micrometric 3D patterns into a polymer layer have been recently studied at Leti. To address new application needs, it is interesting to transfer these structures into silicon based inorganic layers because of their optical properties. Furthermore, the 3D pattern dimensions, currently few micrometers, need to be sub-micrometric for the most advanced technologies. In these condition, pattern transfer fidelity of 3D structures is even more challenging and it underlines why the etch mechanisms need to be well understood.
Currently the transfer into inorganic layers by plasma etching of submicronic 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.
Learning world models for advanced autonomous agent
World models are internal representations of the external environment that an agent can use to interact with the real world. They are essential for understanding the physics that govern real-world dynamics, making predictions, and planning long-horizon actions. World models can be used to simulate real-world interactions and enhance the interpretability and explainability of an agent's behavior within this environment, making them key components for advanced autonomous agent models.
Nevertheless, building an accurate world model remains challenging. The goal of this PhD is to develop methodology to learn world models and study their use in the context of autonomous driving, particularly for motion forecasting and developing autonomous agents for navigation.
Development of a predictive power model for a photovoltaic device under spatial constraints
CEA is developing new cell and module architectures and simulation tools to assess the electrical performance of photovoltaic (PV) systems in their operating environment. One of these models, called CTMod (Cell To Module), takes into account not only the different materials making up the module, but also the different cell architectures. For space applications, the community wants to use terrestrial silicon-based technologies that can be integrated on flexible PVAs (Photovoltaic Assembly). The space environment imposes severe constraints. A relevant evaluation of performance at the start and end of a mission is therefore essential for their dimensioning.
The aim of this thesis is to correlate physical models of radiation-matter degradation in space with electrical models of photovoltaic cells. Performance degradations linked to the various electron, proton and ultraviolet (UV) irradiations of the space environment will be evaluated and validated experimentally. Linked to the CTMod Model, this new approach, never seen in the literature, will able to get a more accurate understanding of interactions between radiations and PVAs. These degradations result from non-ionizing energy deposition phenomena, quantified by the defect dose per displacement, and ionizing ones quantified by the total ionizing dose for protons and electrons. In the case of UV, the excitation of electrons in matter generates chain breaks in organic materials and colored centers in inorganic materials. Initially, the solar cell used in the model will be a Silicon cell, but the model can be extended to include other types of solar cell under development, such as perovskite-based cells.
Multiphe hydrogen injection at anode side of PEMFC
The alternating feeding architecture (known as Ping-Pong) was developed by the CEA. This architecture emerged in 2013 and has been implemented in several fuel cell systems. Following the latest tests on this architecture, questions remained unanswered. First, it is a question of understanding how species (hydrogen, nitrogen, liquid and gaseous water) move in cells operating with alternating feeding. Control laws influences these movements, it will be necessary to identify the levers to make the most out of it and then to propose methods to promote the evacuation of water and nitrogen while avoiding the evacuation of hydrogen.
The thesis work will aim to optimize the anode architecture with alternating feeding and to bring this architecture to maturity. The key points are the search for an optimum control of this architecture, the achievement of a hydrogen rejection rate of less than 1%. Finally, this optimization will also have to maximize the durability of the stack.
The doctoral student will have to model the movements of species at different time scales (10ms to 10 minutes), understand the mechanisms, adapt the control laws and validate the new control laws on a test bench.
This work will identify solutions to efficiently evacuate liquid water and nitrogen and minimize H2 rejection and then obtain superior performance compared to conventional architectures.
Dynamic clamping of hygrogen fuel cells: experimental and numerical simulation approach
The impact of the clamping of PEMFC stacks has been demonstrated by the publication of numerous experimental measurements. Passive clamping systems were developped to garantee the minimum elasticity necessary notably during temperature changes or to improve the stress distribution. The new components are finer and finer presenting a reduced elasticity range, moreover latest publications demonstrate the impact of clamping on the deformation and performance of few microns thick active layers and it should be a major improvement to integrate an accurate dynamic clamping.
The first aim of the phD is to study experimetally the impact of the dynamic control of the clamping on the performances of stacks. These tests will be performed with stacks integrating either stamped metallic bipolar plates: the reference technology, or printed cells: the new technology in development at CEA. In parallel, the candidate will learn the model, actually under development thanks to a phD, simulating stresses and deformations, and the associated multiphysic parameters such as porosity or electric resistance, in function of clamping.
Thanks to the synthesis of these experimental and numerical results the candidate will improve the undertanding of the impact of the clamping and will propose solutions to improve notably the durability which is a critical point for our ongoing european or industrial projects.
In function of the phD progress, vibratory tests could be performed to evaluate the potential input of mechanical spectroscopy, notably for diagnosis.
Secure and Agile Hardware/Software Implementation of new Post-Quantum Cryptography Digital Signature Algorithms
Cryptography plays a fundamental role in securing modern communication systems by ensuring confidentiality, integrity, and authenticity. Public-key cryptography, in particular, has become indispensable for secure data exchange and authentication processes. However, the advent of quantum computing poses an existential threat to many of the traditional public-key cryptographic algorithms, such as RSA, DSA, and ECC, which rely on problems like integer factorization and discrete logarithms that quantum computers can solve efficiently. Recognizing this imminent challenge, the National Institute of Standards and Technology (NIST) initiated in 2016 a global effort to develop and standardize Post-Quantum Cryptography (PQC). After three rigorous rounds of evaluation, NIST announced its first set of standardized algorithms in 2022. While these algorithms represent significant progress, NIST has expressed an explicit need for additional digital signature schemes that leverage alternative security assumptions, emphasizing the importance of schemes that offer shorter signatures and faster verification times to enhance practical applicability in resource-constrained environments. Building on this foundation, NIST opened a new competition to identify additional general-purpose signature schemes. The second-round candidates, announced in October 2024, reflect a diverse array of cryptographic families.
This research focuses on the critical intersection of post-quantum digital signature algorithms and hardware implementations. As the cryptographic community moves toward adoption, the challenge lies not only in selecting robust algorithms but also in deploying them efficiently in real-world systems. Hardware implementations, in particular, must address stringent requirements for performance, power consumption, and security, while also providing the flexibility to adapt to multiple algorithms—both those standardized and those still under evaluation. Such agility is essential to future-proof systems against the uncertainty inherent in cryptographic transitions. The primary objective of this PhD research is to design and develop hardware-agile implementations for post-quantum digital signature algorithms. The focus will be on supporting multiple algorithms within a unified hardware framework, enabling seamless adaptability to the diverse needs of evolving cryptographic standards. This involves an in-depth study of the leading candidates from NIST’s fourth-round competition, as well as those already standardized, to understand their unique computational requirements and security properties. Special attention will be given to designing modular architectures that can support different signatures, ensuring versatility and extensibility. The proposed research will also explore optimizations for resource efficiency, balancing trade-offs between performance, power consumption, and area utilization. Additionally, resilience against physical attacks (side-channel attacks and fault injection attacks) will be a key consideration in the design process. This PhD project will be conducted within the PEPR PQ-TLS project in collaboration with the TIMA laboratory (Grenoble), the Agence nationale de la sécurité des systèmes d’information (ANSSI) and INRIA.