CdTe for medical radiography; control of electrical properties
The use of direct-conversion detectors in medical radiography opens up new possibilities. Due to its properties, the semiconductor material CdTe has emerged as the material of choice for manufacturing these new components. The proposed thesis topic aims to develop the knowledge and processes necessary to produce CdTe crystals with properties tailored to specific application requirements. The work will draw on the laboratory’s advanced expertise in mastering CdTe single-crystal growth processes. The key challenges of the project will be as follows:
- Performing annealing under controlled atmospheres (ex-situ, on small samples) to study their impact on the electrical properties of CdTe,
- Conducting advanced characterizations to better understand the doping mechanisms in CdTe,
- Fabricating “simple” devices and testing them under X-ray flux to quantify the performance of the laboratory’s materials.
The proposed thesis topic is central to the development of a CdTe technology for medical radiography applications. Multidisciplinary work (material and process development, material characterization, fabrication and X-ray testing of simplified devices) is proposed to address this topic.
Energy-minimizing associative neural networks using resistive memories
This PhD project aims to develop Hopfield-type associative neural networks that perform inference through energy-minimizing dynamics.
The goal is to exploit these dynamics for image denoising and reconstruction close to the sensor, under strict energy and latency constraints.
The network synapses will be implemented in ReRAM crossbar arrays, enabling analog in-memory matrix-vector operations.
The work will focus on architecture dimensioning while accounting for array size, weight quantization, device variability and endurance limits.
Reference models will be developed in PyTorch to evaluate alternative neural dynamics and hardware mapping strategies.
Patch-wise image denoising will serve as the main use case to quantify trade-offs between reconstruction quality, latency and energy consumption.
Particular attention will be paid to the robustness of the networks against hardware non-idealities such as noise, variability and memory drift.
The project will also investigate local on-chip learning mechanisms, allowing slow adaptation to changes in the sensor, scene or memory devices.
These learning rules must remain compatible with the endurance constraints of resistive memories.
Ultimately, the PhD should provide hardware-sizing guidelines and support the design of an experimental test vehicle.
The broader scientific objective is to demonstrate that dynamic associative inference can become an efficient, robust and low-power building block for edge AI.
New methodologies for analyzing the impact of crystal defects on the electrical performance of SiC power devices
In our past studies on SiC power devices, the analysis of electrical performances on diodes [1] (idem for future MOSFETs) must take into account the impact of material's defects at the epitaxy and substrate level.
Initially, the thesis work will consist of setting up tools dedicated to our needs in the SiC team. The specifications for these tools have already been established as part of the internship currently underway within the LAPS laboratory. These AI tools will be able to be trained on already existing datasets (SiC diode batches: with electrical data, defect mappings) and complete the previous manually carried out analyses.
In a second phase, the use of the developed tools will be applied to new manufactured and characterized batches. The range of data will then be completed by considering new component architectures (diodes and power MOSFETs), new material characterizations (defects characterization from other tools being installed at Leti, or even with external collaborators: see Line Pilot WBG, see Soitec), new entries (images of defectivity, obtained during the components fabrication in the clean rooms).
Note that the approach applies i) in the case of power to other materials (GaN, diamond, Ga2O3...), ii) also potentially to any component on semiconductor (memory, transistor, photonic, quantum...).
Systemic validation of fuzzy rule bases: accounting for data availability and the specific characteristics of fuzzy inference
This PhD topic lies within the field of symbolic artificial intelligence. Unlike approaches based on neural networks, these methods rely on explicit rules, often provided by experts or learned from limited data, making them interpretable but potentially imperfect.
The central problem is therefore the validation of fuzzy rule bases: the goal is to ensure that the rules produce consistent, useful, and reliable results. Existing methods use global metrics (overall system performance) and local metrics (the quality of each rule), but they do not sufficiently account for certain important specificities. For example, interactions between rules can strongly influence the final behavior.
The thesis proposes to develop a comprehensive and systematic approach to validate these rule bases, whether data is available or not. In particular, it aims to design new metrics capable of capturing these interactions, drawing inspiration, for example, from graph-based approaches (such as FinGrams or reputation systems).
The work will include the definition of a methodological framework, the proposal of new validation measures, as well as their implementation and experimental evaluation.
The expected outcomes are more precise tools for detecting problematic rules, and an overall improvement in the performance and reliability of fuzzy inference systems.
Securing Generative AI Model: Detection of Advanced Backdoor Attacks
This PhD aims to investigate and detect backdoor attacks within generative AI model ecosystems, including standalone models, retrieval-augmented generation systems (RAG), and LLM-based agent. The research will focus on developing novel detection and defense mechanisms against stealthy trigger-based attacks, emphasizing real-world deployment scenarios and robust evaluation benchmarks. In addition to developing defense mechanisms and releasing the code as open source, the thesis also aims to provide the scientific community with a comprehensive evaluation framework.
Context: Many users (persons, institutions, NGOs and even industries) are currently not in a position to develop their own AI agents. Thus, they may download open-source genAI models or LLM-based agents that are typically designed to be highly accessible and user-friendly, requiring minimal to no technical expertise. This practice is widespread due to the large number of open-source models and LLM agent implementations available online (e.g. Hugging Face hosts over two million public models). Unfortunately, the behavioral integrity of the downloaded model is never verified, and the model may have been previously backdoored. There is therefore an urgent need to provide defense mechanisms capable of scanning the components of a generative AI system (models and knowledge bases) and identifying those that have been poisoned.
Objective: The research will focus on developing novel detection and defense mechanisms against stealthy trigger-based attacks, emphasizing real-world deployment scenarios and robust evaluation benchmarks. In addition to developing defense mechanisms and releasing the code as open source, the thesis also aims to provide the scientific community with a comprehensive evaluation framework.
Junction defect characterization of low therMal Budget SOI MoSFET
Join CEA-Leti and CROMA to analyze in depth junctions of a new technology. Indeed, our transistors are fabricated under restricted thermal budget for 3D sequential integration, making dopants activation very challenging! Our team will support you technically and scientifically to conduct this work. Some data are already available and waiting for your analysis.
During this PhD, you will have the opportunity to perform all theses steps:
From the idea (simulation, bibliography, TCAD) 20%
Processes understanding (implantation, SPER) 10%
Integration & cleanroom fabrication management 10%
Characterization (physical & electrical: noise, DLTS…) 50%
Valorization (presentations, article) 10%
This PhD offers a unique chance to be at the forefront of technological innovation and to make a significant impact in the field of advanced SOI. Join us and take the first step towards an exciting career in research and development!
With a background in microelectronics or nanotechnologies, you are curious about integration of new processes, not afraid about equations and liked semiconductors classes at school. You want to solve complex puzzles and enjoy collaborating with others to figure out innovative solutions.
Growth of Inorganic Halide Perovskite 2D/3D Heterostructures via Pulsed Laser Deposition (PLD) for Optoelectronics and Photovoltaics
Halide perovskites (HPs) have demonstrated exceptional potential in photovoltaics (PV), achieving record efficiencies (35% in silicon-based tandem cells). However, their limited stability (degradation under humidity, heat, or light) and scalability challenges (efficiency loss at large scale) hinder industrial adoption. Concurrently, in microLED applications, HPs are emerging as a promising alternative to quantum dots (QDs) for color conversion layers, thanks to their high spectral purity and superior absorption. Yet, their efficiency and stability still require optimization to compete with existing solutions.
This project proposes an innovative approach: fabricating inorganic 2D perovskites and 2D/3D heterostructures via pulsed laser deposition (PLD), a scalable and unexplored method for perovskites. 2D perovskites, due to their quantum confinement, exhibit high exciton binding energy, making them ideal for LEDs and lasers, while 2D/3D heterostructures enhance stability and reduce non-radiative recombination.
The thesis objectives are:
1. Synthesis of inorganic 2D perovskites (lead-free and lead-based) via PLD and advanced material characterization (crystallinity, luminescence, absorption, bandgap, stability).
2. Fabrication of 2D/3D heterostructures to achieve defect passivation in 3D layers, with advanced characterization (photoluminescence yield, carrier lifetime, interface passivation).
3. Application in PV and microLEDs: evaluating potential for tandem solar cells and color conversion layers.
The results aim to demonstrate that PLD can overcome current limitations (stability, large-scale production) while maintaining competitive optoelectronic performance. This work aligns with global efforts where perovskites could drive significant advancements in PV and microdisplays
Low Power Image Sensor for Distributed Processing in Cameras Network
Working in a collaborative academic project, your task will be to develop a smart image sensor for a wireless camera network embedding distributed AI computing.
Current camera network contains several standard cameras that transmit their images to a global server performing the targeted inference processing. This kind of architecture proposes energy and frugality performances that are not compatible with IoT requirements.
The project goal is to tackle hardware frugality through a distributed and collaborative approach based on ultra-low-power computing nodes. Each node’s inference core will be built around ASIC processors performing calculations in analog form. The final demonstrator will consist of a wireless network of “motes” (sensor network nodes) integrating dedicated image sensors paired with hybrid processors performing analog processing.
In this context, the mote’s image sensor must extract strategic features with frugality and efficiency which implies that you have to define, design and test an innovative readout architecture of a standard imager. In collaboration with the academic partners, you will be involved in the definition of the overall mote architecture allowing to define basically the output data format and the output procedure of the imager including potential pre-processing for the distributed inference computations. The studied architecture will integrate innovative low power solutions to address the targeted IoT applications and perform both image acquisitions and AI pre-processing.
As an image sensor demonstrator is planned in this PhD Thesis, the work will be conducted at CEA-Leti in the L3i Laboratory, using professional IC design tools and software development environments.
Post-training neural architecture optimization for small language models
Generative AI, and particularly language models (LLM), have sparked a new revolution in AI with applications across all domains. However, LLMs are highly resource-intensive and, hence, difficult to implement on autonomous embedded systems. LLMs can be optimized by modifying their architecture to replace heavy Transformer layers with lighter alternatives. Given the difficulty of training LLM "from scratch," this thesis aims to develop post-training neural architecture optimization methods applicable to small LLM (SLM). Additionally, the thesis seeks to propose performance metrics of different layers of an SLM and their alternatives, to guide the replacement, and thus propose a comprehensive methodology for optimizing SLMs while considering hardware constraints. The work will be valorized through publications in major AI conferences and journals, and the developed codes and methods could be integrated into the tools developed at CEA.
Development of an innovative anode based on non-critical and sustainable materials for anion-exchange membrane electrolysis
Anion-exchange membrane water electrolysis (AEMWE) is a recent and promising technology for producing green hydrogen, but it still faces major challenges in terms of performance and durability. Currently, the anodes used in AEMWE electrolyzers consist of two layers: a porous transport layer (PTL), which enables the circulation of electrolyte and gases, and an active layer made of catalysts and binders, where the electrochemical reactions take place. This configuration limits reactant diffusion and reduces the available active surface area, which negatively impacts overall performance.
This PhD project aims to develop an innovative anode based on non-critical materials by combining the advantages of both layers while minimizing their drawbacks. The idea is to functionalize the PTL directly by adding catalyst nanoparticles and/or by applying a surface activation treatment, in order to confer electrochemical activity. These modifications are expected to improve electron and reactant transport while increasing the active surface area for the oxygen evolution reaction (OER).
The work carried out in this thesis will involve functionalizing a pre-selected PTL and characterizing the resulting anodes through structural and electrochemical analyses. The expected outcomes include the development of an optimized anode with enhanced performance and limited degradation, as well as a deeper understanding of the limiting phenomena in AEMWE anodes. This project is part of a broader effort to develop sustainable technologies essential for the energy transition.