Integrated optical functions on microbolometer focal planes for uncooled infrared imaging
Thermal infrared imaging (wavelengths 8-14 µm) is a growing field, particularly in industry, transportation, and environment. It relies on a detection technology, microbolometers, for which CEA-Leti is at the forefront of the global state of the art. Integrating advanced optical functions directly onto the detectors is a very promising approach for improving performance, compactness, and cost in future infrared cameras.
The optical functions under consideration include spectral filtering, polarimetry, wavefront correction, and more. Some aim to enrich the image with information essential for applications such as absolute thermography (temperature and emissivity measurement), identification for automated scene interpretation (machine vision), gas detection, and others.
The proposed work will include the design, fabrication, and electro-optical characterization of functionalized microbolometer arrays. Using 3D electromagnetic simulation tools, the design of these optical functions will take into account the compatibility with our microbolometer technologies and the capabilities of our microfabrication facilities. Fabrication will take place in the CEA-Leti cleanrooms by dedicated personnel, but the candidate will participate in defining and monitoring the work. Finally, optical and electro-optical characterizations will be performed in our laboratory, if necessary with the development of dedicated characterization benches.
Hydrodynamic simulations of porous materials for ductile damage
The mechanical behavior of metallic materials under highly dynamical loading (schock) and especially their damage behavior is a topic of interest for the CEA-DAM. For tantalum, damage is ductile : by nucleation, growth and coalescence of voids within the material. Usual ductile damage models have been developed using the simplifying assumption that voids are isolated in the materials. However, recent studies by direct simulations explicitly describing a void population in the material (and experimental observations after failure) have shown the importance of void interaction for predicting ductile damage. Yet, the microscopical mechanisms of this interaction remain little known.
The objective of the PhD is to study the growth and coalescence phases of ductile damage through direct numerical simulations of a porous material undergoing dynamic loading. Hydrodynamic simulations, in which voids are explicitly meshed within a continuous matrix, will be used to study relevant scales of length and time. Monitoring the void population throughout the simulation will provide valuable information on the influence of void interaction during ductile damage. Firstly, the bulk behavior will be compared to the one predicted by usual models of isolated voids, showing the macroscopic effect of void interaction. Secondly, the evolution of the size distribution in the void population will be monitored. The last objective will be to understand microscopic void-to-void interaction. In order to take advantage of the wealth of simulation results, approaches based on artificial intelligence (neural networks on the graph associated with the pore population) will be used to learn the link between a void's neighborhood and its growth.
The doctoral student will have the opportunity to develop their skills in shock physics and mechanics, numerical simulations (with access to CEA-DAM supercomputers), and data science.
Modelling of Thermo-Fluid Phenomena in the Plasma Nozzle of the ELIPSE Process
The ELIPSE process (Elimination of Liquids by Plasma Under Water) is an innovative technology dedicated to the mineralization of organic effluents. It is based on the generation of a thermal plasma fully immersed in a water-filled reactor vessel, enabling extremely high temperatures and reactive conditions that promote the complete decomposition of organic compounds.
The proposed PhD research aims to develop a multiphysics numerical model describing the behavior of the process, particularly within the plasma nozzle, a key zone where the high-temperature gas jet from the torch interacts with the injected liquids.
The approach will rely on coupled thermo-aerodynamic modeling, integrating fluid dynamics, heat transfer, phase change phenomena, and turbulence effects. Using Computational Fluid Dynamics (CFD) tools, the study will characterize plasma–liquid interaction mechanisms and optimize the geometry and operating conditions of the process. This modeling will be compared and validated against complementary experimental data obtained from the ELIPSE setup, providing the necessary input for model calibration and validation.
This work will build upon previous research that has led to the development of thermal and hydraulic models of both the plasma torch and the reactor vessel. Integrating the new model within this framework will yield a comprehensive and coherent representation of the ELIPSE process. Such an approach represents a decisive step toward process optimization and industrial scale-up.
The ideal candidate will be a Master’s or final-year engineering student with a background in process engineering and/or numerical simulation, demonstrating a strong interest in physical modeling and computational approaches.
During this PhD, the candidate will develop and strengthen skills in multiphysics numerical modeling, advanced CFD simulation, and thermo-aerodynamic analysis of complex processes. They will also acquire solid experience in waste treatment, a rapidly expanding field with significant industrial and environmental relevance. These skills will provide strong career opportunities in applied research, process engineering, energy, and environmental sectors.
AI Enhanced MBSE framework for joint safety and security analysis of critical systems
Critical systems must simultaneously meet the requirements of both Safety (preventing unintentional failures that could lead to damage) and Security (protecting against malicious attacks). Traditionally, these two areas are treated separately, whereas they are interdependent: An attack (Security) can trigger a failure (Safety), and a functional flaw can be exploited as an attack vector.
MBSE approaches enable rigorous system modeling, but they don't always capture the explicit links between Safety [1] and Security [2]; risk analyses are manual, time-consuming and error-prone. The complexity of modern systems makes it necessary to automate the evaluation of Safety-Security trade-offs.
Joint safety/security MBSE modeling has been widely addressed in several research works such as [3], [4] and [5]. The scientific challenge of this thesis is to use AI to automate and improve the quality of analyses. What type of AI should we use for each analysis step? How can we detect conflicts between safety and security requirements? What are the criteria for assessing the contribution of AI to joint safety/security analysis?
Influence of battery system disassemblability on their environmental impacts
With the rise of electric mobility and Energy storage, the demand for batteries is rapidely increasing. But this growth raises a crucial question: how can we design batteries that are both high-performing, durable, and more environmentaly friendly ?
Without focusing on cell Chemistry, one promising approach lies in disassembly-oriented designs: making battery packs easier to disassemble could facilitate their repair, reuse, or recycling. However, a more easily dismantled design may also increase its mass or reduce the system's reliability, potentially affecting its overall lifetime.
This PhD aims to tackle this challenge by developing an analytical method to link the design of dismountable battery systems with their actual environmental impacts, while explicitly accounting for reliability aspects.
The PhD candidate will assess the ease of disassembly of different battery systems, quantify the environmental gains and losses compared to conventional designs, and help develop a decision-support tool to guide design choices. The proposed research will involve, among other tasks, Life Cycle Assessment (LCA) modelling coupled with battery performance and ageing models, as well as failure probabilities analysis.
This project takes place in a technological context driven by the growing need for resource circularity, the automation of disassembly processes, and the implementation of new European regulations on batteries. If offers a unique opportunity to contribute to the design of the next generation of sustainable battery systems.
CORTEX: Container Orchestration for Real-Time, Embedded/edge, miXed-critical applications
This PhD proposal will develop a container orchestration scheme for real-time applications, deployed on a continuum of heterogeneous computing resources in the embedded-edge-cloud space, with a specific focus on applications that require real-time guarantees.
Applications, from autonomous vehicles, environment monitoring, or industrial automation, applications traditionally require high predictability with real-time guarantees, but they increasingly ask for more runtime flexibility as well as a minimization of their overall environmental footprint.
For these applications, a novel adaptive runtime strategy is required that can optimize dynamically at runtime the deployment of software payloads on hardware nodes, with a mixed-critical objective that combines real-time guarantees with the minimization of the environmental footprint.
Effect of gamma-ray irradiation on ferroelectric, hafnia-based, non-volatile memory for use in extreme environments
The emergence of hafnia-based ferroelectric (FE) memories has opened a new paradigm for ultra-low-power edge computing. Hafnia is fully compatible with CMOS technology and is ultra low-power—three orders of magnitude less than other emerging memory technologies.
These advantages align with strategic applications in space, defense, medical, nuclear safety, and heavy-duty transport, where electronics face harsh radiation environments.
Imprint induces a shift of the Polarization-Voltage (P-V) curve along the voltage axis and is attributed to charge trapping/detrapping, domain pinning and charged defects. All may be accentuated under irradiation.
The project will use advanced photoelectron spectroscopy techniques including synchrotron radiation induced Hard X-ray photoelectron spectroscopy and complementary structural analysis including high-resolution electron microscopy, X-ray diffraction and near field microscopy. The experimental characterization will be accompanied by theoretical calculations to simulate the material response to irradiation
The work will be carried out in the framework of close collaboration between the CEA/Leti in Grenoble providing the samples, integrated devices and wafer scale characterization and the CEA/Iramis in Saclay for the fundamental analysis of the material properties, irradiation experiments and device scale characterizations.
3D interconnects for the design and fabrication of quantum processor units
To increase the performance of quantum computers, three-dimensional (3D) integration is now the key! Using technologies such as flip-chip bonding, multi-layer wiring or even through-silicon vias (TSV), 3D integration offers solutions to increase the number of qubits on a processor, reduce signal loss and cross-talk and even improve thermal management. All of these aspects are essential to continue scaling qubits to achieve fault-tolerant quantum computing.
Our team is developing 3D interconnect technologies (e.g. superconducting microbumps and TSV) for the next generation of quantum processors. This thesis will focus on the electrical and radiofrequency characterization of such interconnects and of the quantum devices integrated nearby to gain knowledge on how these 3D technological bricks may impact the quantum properties.
This position will bring you at the boundary between material, technological and physical challenges of quantum systems. You will work with teams from CEA-LETI and CEA-IRIG. As a PhD candidate, you will take part in the design and layout of test vehicles and in their fabrication. You will also lead the low temperature measurements of the fabricated samples, perform the associated analysis and write reports.
A theoretical framework for the task-based optimal design of Modular and Reconfigurable Serial Robots for rapid deployment
The innovations that gave rise to industrial robots date back to the sixties and seventies. They have enabled a massive deployment of industrial robots that transformed factory floors, at least in industrial sectors such as car manufacturing and other mass production lines.
However, such robots do not fit the requirements of other interesting applications that appeared and developed in fields such as in laboratory research, space robotics, medical robotics, automation in inspection and maintenance, agricultural robotics, service robotics and, of course, humanoids. A small number of these sectors have seen large-scale deployment and commercialization of robotic systems, with most others advancing slowly and incrementally to that goal.
This begs the following question: is it due to unsuitable hardware (insufficient physical capabilities to generate the required motions and forces); software capabilities (control systems, perception, decision support, learning, etc.); or a lack of new design paradigms capable to meet the needs of these applications (agile and scalable custom-design approaches)?
The unprecedented explosion of data science, machine learning and AI in all areas of science, technology and society may be seen as a compelling solution, and a radical transformation is taking shape (or is anticipated), with the promise of empowering the next generations of robots with AI (both predictive and generative). Therefore, research can tend to pay increasing attention to the software aspects (learning, decision support, coding etc.); perhaps to the detriment of more advanced physical capabilities (hardware) and new concepts (design paradigms). It is however clear that the cognitive aspects of robotics, including learning, control and decision support, are useful if and only if suitable physical embodiments are available to meet the needs of the various tasks that can be robotized, hence requiring adapted design methodologies and hardware.
The aim of this thesis is thus to focus on design paradigms and hardware, and in particular on the optimal design of rapidly-produced serial robots based on given families of standardized « modules » whose layout will be optimized according to the requirements of the tasks that cannot be performed by the industrial robots available on the market. The ambition is to answer the question of whether and how a paradigm shift may be possible for the design of robots, from being fixed-catalogue to rapidly available bespoke type.
The successful candidate will enrol at the « Ecole Doctorale Mathématiques, STIC » of Nantes Université (ED-MASTIC), and he or she will be hosted for three years in the CEA-LIST Interactive Robotics Unit under supervision of Dr Farzam Ranjbaran. Professors Yannick Aoustin (Nantes) and Clément Gosselin (Laval) will provide academic guidance and joint supervision for a successful completion of the thesis.
A follow-up to this thesis is strongly considered in the form of a one-year Post-Doctoral fellowship to which the candidate will be able to apply, upon successful completion of all the requirements of the PhD Degree. This Post-Doctoral fellowship will be hosted at the « Centre de recherche en robotique, vision et intelligence machine (CeRVIM) », Université Laval, Québec, Canada.
Artificial Intelligence for the Modeling and Topographic Analysis of Electronic Chips
The inspection of wafer surfaces is critical in microelectronics to detect defects affecting chip quality. Traditional methods, based on physical models, are limited in accuracy and computational efficiency. This thesis proposes using artificial intelligence (AI) to characterize and model wafer topography, leveraging optical interferometry techniques and advanced AI models.
The goal is to develop AI algorithms capable of predicting topographical defects (erosion, dishing) with high precision, using architectures such as convolutional neural networks (CNN), generative models, or hybrid approaches. The work will include optimizing models for fast inference and robust generalization while reducing manufacturing costs.
This project aligns with efforts to improve microfabrication processes, with potential applications in the semiconductor industry. The expected results will contribute to a better understanding of surface defects and the optimization of production processes.