Development of ultra-high-resolution magnetic microcalorimeters for isotopic analysis of actinides by X-ray and gamma-ray spectrometry
The PhD project focuses on the development of ultra-high-resolution magnetic microcalorimeters (MMCs) to improve the isotopic analysis of actinides (uranium, plutonium) by X- and gamma-ray spectrometry around 100 keV. This type of analysis, which is essential for the nuclear fuel cycle and non-proliferation efforts, traditionally relies on HPGe detectors, whose limited energy resolution constrains measurement accuracy. To overcome these limitations, the project aims to employ cryogenic MMC detectors operating at temperatures below 100 mK, capable of achieving energy resolutions ten times better than that of HPGe detectors. The MMCs will be microfabricated at CNRS/C2N using superconducting and paramagnetic microstructures, and subsequently tested at LNHB. Once calibrated, they will be used to precisely measure the photon spectra of actinides in order to determine the fundamental atomic and nuclear parameters of the isotopes under study with high accuracy. The resulting data will enhance the nuclear and atomic databases used in deconvolution codes, thereby enabling more reliable and precise isotopic analysis of actinides.
AI model deployment using Hardware-Aware on-chip Fine Tuning
Emerging unconventional hardware technologies are essential for future Edge-AI applications, but they often suffer from variability, mismatches, and technology dispersion. These non-idealities can strongly reduce AI inference accuracy if no fine-tuning or calibration is applied. Traditional supervised fine-tuning is difficult to industrialize because it raises issues related to data confidentiality, service quality, software complexity, and hardware constraints.
This PhD project aims to develop hardware-algorithm co-design methods that avoid the need for fully supervised on-chip retraining. The main goal is to create task-agnostic, inference-level self-calibration strategies able to compensate hardware mismatches at the system level. The work will study existing adaptation methods, including weight-based, feature-based, output-based, and domain adaptation approaches.
The project will define a relevant Edge-AI application, develop a generic fine-tuning method, and validate it through low-level electrical simulations. If possible, the proposed algorithm may also be tested experimentally on a custom ASIC-based hardware setup.
Self-calibrated mmW Injection Locked Oscillators
In our research group we have developed and used during the last few year an innovative technique for frequency generation where the injection locked oscillator are the core. However, this circuits that we found in electronics, but also in other disciplines such as mechanics, biology and fundamental physics, hide still some secrets.
In this PhD you will be starting with the existing knowledge about these circuits leveraging in the broad experience of the research team and you will contribute to extend this knowledge to understand the impact of external perturbations and manufacturing tolerances on the operation of such circuit, for next proposing and implementing self-calibration and stabilization techniques.
The. applications of this circuits are broad, but some of the most appealing are found in the field of high-speed mmW wireless and wired links, extremely high resolution radar, vital signs detection for medical applications and quantum experiments (such as resonant paramagnetic spectrometry.
Depending on the advancement of the research the proposed self-calibration technique will be applied in one of the existing developments in our group in one of these fields.
How defects nucleation affects the the fracture on the SmartCut process
The SmartCut™ technology is widely used in microelectronics for the fabrication of innovative substrates, such as SOI (Silicon-on-Insulator).
The physical phenomena underlying SmartCut™ technology remain one of principal interest of our research. Optimizing the fracture stage is a major focus in our laboratory and in our collaboration with Soitec. Salomon's PhD thesis (expected completion December 2026), the development of post-fracture surface analysis protocols highlighted the link between the evolution of cristalline defects that cause fracture (platelets) and post-fracture surface roughness. We were thus able to characterize the early stages of platelet growth and determine their main characteristics (size and density). This had previously only been achieved through complex characterizations based on TEM observations.
Now that we have highlighted the impact of platelets on post-fracture surface roughness, the next step is to investigate and identify ways to control their nucleation using new processes. This will also involve optimizing the post-fracture state of SOI substrates.
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.