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.
AI-Driven Design & Optimization of Load-Modulated PAs for 6G Applications
The proposed PhD research focuses on the AI-driven design and optimization of load-modulated power amplifiers (PAs) for next-generation 6G wireless systems, where extreme data rates, energy efficiency, and linearity are paramount. The study aims to integrate machine learning and evolutionary optimization techniques into the PA design flow to enable intelligent trade-offs between efficiency, bandwidth, and linearity under dynamic load modulation. Physics-based circuit modeling will be coupled with surrogate modeling and reinforcement learning to explore vast design spaces with reduced computational cost. Emphasis will be placed on real-time load modulation architectures (e.g., Doherty, Outphasing, and hybrid topologies) implemented in advanced semiconductor technologies such as GaN and InP. The project further seeks to establish AI-guided co-optimization frameworks linking device-level characteristics with system-level performance. Expected outcomes include novel design methodologies, compact behavioral models, and experimentally validated prototypes demonstrating high efficiency across wide modulation bandwidths, paving the way toward sustainable, adaptive, and intelligent 6G RF front-ends.
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.
An electrochemical flow microreactor for a greener synthesis of gold nanoparticles
Gold nanoparticles (AuNPs) possess unique electronic, photonic, and chemical properties of invaluable interest in a variety of medical and technological applications. They are typically produced by controlled chemical precipitation from a salt solution to achieve the precise size control critical for most applications. Continuous flow microreactors, which efficiently mix the salt solution and the reducing agent, are known to offer improved size control. However, even in these reactors, the smallest AuNPs can only be formed using powerful reducing agents that are harmful to human health or the environment. We propose to minimize their impact and to develop a more resource-efficient process by inserting an electrochemical cell into the reactor to form the reducing agent in-situ in the adjusted amount necessary to produce the desired AuNPs.
Your goal will be to test and adapt continuous-flow electrochemical cells for the synthesis of AuNPs, exploring various electrochemical reactions and cell designs. You will also explore the use of several capping agents of biological interest. A careful examination of AuNPs characteristics (size, interfacial and optical properties, etc.) will guide you in this research.
Multi-scale approach for ultrasonic propagation in inhomogeneous multiple-scattering media
Ultrasonic waves are strongly influenced by the microstructure of the materials through which they propagate, leading to attenuation, dispersion, and noise. Modeling these effects is essential, particularly in non-destructive testing, where they may either hinder defect detection or provide valuable information about the material. Analytical and numerical models help to better predict and interpret these phenomena. Homogeneous statistical properties are generally assumed in such approaches. In practice, however, microstructures often exhibit significant spatial variations, for instance due to manufacturing processes. Depending on the scale of these variations relative to the wavelength, they may induce either abrupt or gradual changes in effective properties. This PhD aims to establish a theoretical framework that accounts for both microstructural randomness and its spatial variations, in order to propose relevant simulation strategies depending on the scales involved. The approach will first be developed in 1D, then extended to 2D and 3D using tools developed in the laboratory, with numerical and possibly experimental validations.
Control coordination of power converters on the distribution grid to enhance overal system stability
With the increasing number of generation and consumption units connected through power electronic converters, the electrical grid is evolving toward a more dynamic and decentralized structure. This transformation strengthens both the need and the potential for these converters to actively contribute to system flexibility and stability—particularly in compensating for renewable energy fluctuations and maintaining the balance between supply and demand.
Optimized coordination of their control functions offers significant potential to improve grid resilience, by intelligently leveraging their capabilities in voltage regulation, frequency support, and reactive power control. However, to integrate these contributions effectively at scale, it is essential to develop holistic modeling approaches that capture multi-scale interactions—both in time and space.
The modeling work in this thesis aims to represent the relationship between the active/reactive power flexibility of power electronic converters and the stability margin they provide to the grid, as well as to model the aggregation of their actions for system-wide contribution. Building on this foundation, coordinated control architectures and algorithms between the distribution and transmission networks will be investigated, developed, and validated.
Prediction of elastic wave dispersion effects using a semi-analytical model under high-frequency approximation
Ultrasonic testing (UT) methods are a fundamental component of non-destructive testing (NDT). They are widely used to inspect mechanical components such as welds (in nuclear and petrochemical industries) and composite material structures (in aeronautics). To understand the physical phenomena involved in a given configuration, simulation is a valuable tool and sometimes an essential step in implementing the inspection process.
Modeling approaches fall into two main categories: purely numerical models based on finite elements (FE) and semi-analytical methods derived from high-frequency (HF) approximations, such as paraxial rays. While the latter are often favored for their computational efficiency, they introduce simplifications that can compromise the quantitative accuracy of results, particularly for phenomena like dispersion (variation in wave speed with frequency), which are common in certain industrial contexts.
This thesis project aims to enhance the paraxial ray approach by integrating models of dispersive interfaces (composite interplies, coupling layers), dispersive viscoelastic media, and a modal guided wave model. The goal is to develop a simulation tool capable of faithfully reproducing realistic inspection configurations, thereby improving the representativeness of the results.