Selective deposition of oxides by ALD
For next-generation microelectronics, Area Selective Deposition (ASD)is a promising approach to simplify integration schemes for the most advanced technology nodes. These ASD approaches need to be adapted according to a trio comprising the material to be deposited, the growth surface, and the inhibited surface.
This PhD focuses on the area selective deposition of oxides (such as SiO2, Al2O3, …) on Si or SiO2 and not on silicon nitride (SiN), which is one of the most complex topics in ASD, and aims to evaluate the relevance of this type of process for simplifying the integration and the fabrication of advanced FDSOI transistors.
To develop this selective oxide deposition process, various approaches aiming at making SiN an inhibitor of the Atomic Layer Deposition (ALD) will be explored (plasma treatments, Small Molecular Inhibitors, combination of both, etc.). Dedicated surface characterizations will be carried out in order to better understand the mechanisms of inhibition at the origin of the selective deposition and allowing to achieve high selectivity for oxide thicknesses of 10 nm and above.
This PhD project will take place at CEA-LETI, within the advanced materials deposition department, in collaboration with LMI UMR 5615 CNRS/UCBLyon. The student will have access to the CEA-LETI 300 mm cleanroom fabrication platforms for thin film deposition by PEALD, the CEA nanocharacterization platform and gas-phase surface functionalization at LMI. Surface analyses and thin film characterizations (ellipsometry, XRR, AFM, FTIR, contact angle, SEM, XPS, ToF-SIMS) will be used to determine the best selectivity and understand the physico-chemical mechanisms.
In situ and real-time characterization of nanomaterials by plasma spectroscopy
The objective of this Phd is to develop an experimental device to perform in situ and real time elemental analysis of nanoparticles during their synthesis (by laser pyrolysis or flame spray pyrolysis). Laser-Induced Breakdown Spectroscopy (LIBS) will be used to identify the different elements present and their stoichiometry.
Preliminary experiments conducted at LEDNA have shown the feasibility of such a project and in particular the acquisition of a LIBS spectrum of a single nanoparticle. Nevertheless, the experimental device must be developed and improved in order to obtain a better signal to noise ratio, to increase the detection limit, to take into account the different effects on the spectrum (effect of nanoparticle size, complex composition or structure), to automatically identify and quantify the elements present.
In parallel, other information can be sought (via other optical techniques) such as the density of nanoparticles, the size or shape distribution.
Development of a new numerical scheme, based on T-coercivity, for discretizing the Navier-Stokes equations.
In the TrioCFD code, the discretization of the Navier-Stokes equations leads to a three-step algorithm (see Chorin'67, Temam'68): velocity prediction, pressure solution, velocity correction. If an implicit time discretization scheme is to be used, the pressure solution step is particularly costly. Thus, most simulations are performed using an explicit time scheme, for which the time step depends on the mesh size, which can be very restrictive. We would like to develop an implicit time discretization scheme using a stabilized formulation of the Navier-Stokes problem based on explicit T-coercivity (see Ciarlet-Jamelot'25). It would then be possible to solve an implicit scheme directly without a correction step, which could significantly improve the performance of the calculations. This would also allow the use of the P1-P0 finite element pair, which is frugal in terms of degrees of freedom but unstable for a classical formulation.
Numerical and experimental study of cryogenic refrigeration system for HTS-based nuclear fusion reactors
The challenge of climate change and the promise of CO2-free energy production are driving the development of new nuclear fusion reactor concepts that differ significantly from systems such as ITER or JT60-SA [R1]. These new fusion reactors push the technological boundaries by reducing investment and operating costs through the use of high-temperature magnets (HTS) to confine the plasma [R4]. These HTS promise to achieve high-intensity magnetic fields while operating at higher cooling temperatures, thereby reducing the complexity of cryogenic cooling, which is normally achieved by forced circulation of supercritical helium at approximately 4.5 K (see 1.8 K for WEST/Tore Supra) delivered by a dedicated cryogenic plant.
The pulsed operation of tokamaks induces a temporal variation in the thermal load absorbed by the cooling system. This operating scenario has led to the development of several load smoothing techniques to reduce the amplitude of these thermal load variations, thereby reducing the size and power of the cooling system, with beneficial effects on cost and environmental impact. These techniques use liquid helium baths (at approximately 4 K) to absorb and temporarily store some of the thermal energy released by the plasma pulse before transferring it to the cryogenic installation [R5].
The objective of this thesis is to contribute to the development of innovative concepts for the refrigeration of large HTS systems at temperatures between 5 and 20 K. It will include (1) the modeling of cryogenic system and cryodistribution architectures as a function of the heat transfer fluid temperature, and (2) the exploration of innovative load smoothing techniques in collaboration with the multidisciplinary "Fusion Plant" team of the PEPR SUPRAFUSION project. The first part will involve the development and improvement of 0D/1D numerical tools called Simcryogenics, based on Matlab/Simscape [R6], through the implementation of physical models (closure laws) and the selection of appropriate modeling techniques to analyze and compare suitable architectural solutions. The second part will be experimental and will involve conducting load smoothing experiments using an existing cryogenic loop operating between 8 and 15 K.
This activity will be at the forefront of the nuclear fusion revolution currently underway in Europe [R3, R7] and the United States [R4], addressing a wide range of cryogenic engineering fields such as refrigeration technologies, superfluid helium, thermo-hydraulics, materials properties, system and subsystem design, and the design and execution of cryogenic tests. It will thus be useful for the development of new generations of particle accelerators using HTS magnets.
[R1] Cryogenic requirements for the JT-60SA Tokamak https://doi.org/10.1063/1.4706907]
[R2] Analysis of Cryogenic Cooling of Toroidal Field Magnets for Nuclear Fusion Reactorshttps://hdl.handle.net/1721.1/144277
[R3] https://tokamakenergy.com/our-fusion-energy-and-hts-technology/fusion-energy-technology/
[R4] https://tokamakenergy.com/our-fusion-energy-and-hts-technology/hts-business/
[R5] “Forced flow cryogenic cooling in fusion devices: A review” https://doi.org/10.1016/j.heliyon.2021.e06053
[R6] “Simcryogenics: a Library to Simulate and Optimize Cryoplant and Cryodistribution Dynamics”, 10.1088/1757-899X/755/1/012076
[R7] https://renfusion.eu/
[R8] PEPR Suprafusion https://suprafusion.fr/
Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment
Power converters are essential in numerous applications such as industry, photovoltaic systems, electric vehicles, and data centers. Their conventional maintenance is often based on fixed schedules, leading to premature replacement of components and significant electronic waste.
This PhD project aims to develop a novel non-invasive and low-cost ultrasound-based monitoring approach to assess the state of health and remaining useful life (RUL) of power converters deployed across various industries.
The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance strategies executed locally (edge deployment).
The research will be carried out within a Marie Sklodowska-Curie Actions (MSCA) Doctoral Network, offering international training, interdisciplinary collaboration, and secondments at leading academic and industrial partners across Europe (Italy and Netherlands for this PhD offer).
Development of automatic gamma spectrum analysis using a hybrid machine learning algorithm for the radiological characterization of nuclear facilities decommissioning.
The application of gamma spectrometry to radiological characterization in nuclear facility decommissioning, requires the development of specific algorithms for automatic gamma spectrum analysis. In particular, the classification of concrete waste according to its level of contamination, is a crucial issue for controlling decommissioning costs.
Within CEA/List, LNHB, in collaboration with CEA/DEDIP, has been involved for several years in the development of tools for the automatic analysis of low-statistics gamma spectra, which can be applied to scintillator detectors (NaI(Tl), plastics). In this context, an original approach based on a hybrid machine learning/statistics spectral unmixing algorithm has been developed for the identification and quantification of radionuclides in the presence of significant deformations in the measured spectrum, due in particular to interactions between the gamma emission from the radioactive source and its environment.
The proposed subject follows on from thesis work that led to the development of the hybrid algorithm with the aim of extending this approach to the radiological characterization of concrete surfaces. The candidate will be involved in the evolution of the hybrid machine learning/statistical algorithm for the characterization of concrete for classification as conventional waste. The work will include a feasibility study of modeling the deviations of the learned model to optimize the robustness of decision-making.
Multiscale modelling of twinning in tin
Twinning is a displacive deformation mechanism characterized by a continuous deformation of the material. Although widely studied for other industrial materials such as titanium alloys, this inelastic mechanism remains poorly understood and incompletely modeled for complex crystallographic structures. However, due to the reduced number of symmetries in these structures, dislocation slip is insufficient to accommodate deformation in certain loading directions, requiring the activation of twinning. This is the case for tin, which has a tetragonal structure. In particular, twinning contributes significantly to the mechanical response of tin at high strain rates and low temperatures. At intermediate temperatures and strain rates, a competition between dislocation plasticity and twinning plasticity can occur, making it crucial to describe the coupling between these two phenomena. Proposing a better description of this coupling will shed new light on the experimental data available at CEA DAM. The objective of the thesis is to develop a multiscale approach, from molecular dynamics to continuum mechanics, validated by experiments, to converge on a model that describes the behavior of tin over a wide range of temperatures and strain rates.
Novel architecture and signal processing for mobile optical telecommunications
Free-Space Optical Communications (FSO) rely on transmitting data via light between two distant points, eliminating the need for fibers or cables. This approach is particularly valuable when wired connections are impractical or prohibitively expensive.
However, these links are highly susceptible to atmospheric conditions—fog, rain, dust, and thermal turbulence—which attenuate or distort the light beam, significantly degrading communication quality. Current solutions remain costly and limited, both in terms of optical compensation hardware and signal processing algorithms.
Within this framework, the thesis aims to design high-performance, robust mobile optical links capable of adapting to dynamic and disturbed environments. The study will focus on leveraging Silicon-based Optical Phased Arrays (OPAs)—a technology derived from low-cost LiDAR systems—offering a promising path toward compact, integrated, and cost-effective architectures.
The primary focus of the research will be developing advanced algorithmic approaches for signal processing and compensation. The PhD candidate will be tasked with designing a dedicated simulation environment to evaluate and validate architectural choices and algorithmic strategies before practical experimentation.
The overarching goal is to propose an integrated, flexible, and reliable architecture that ensures uninterrupted optical communication in motion, with potential applications in aerospace, space, and terrestrial domains.
Integrated material–process–device co-optimization for the design of high-performance RF transistors on advanced nanometer technologies
This PhD research focuses on the integrated co-optimization of materials, fabrication processes and device architectures to enable high-performance RF transistors on advanced nanometer-scale technologies. The work aims to understand and improve key RF figures of merit—such as transit frequency, maximum oscillation frequency, noise behaviour and linearity—by establishing clear links between material choices, process innovations and transistor design.
The project combines experimental development, structural and electrical characterization, and advanced TCAD simulations to analyse the strengths and limitations of different integration schemes, including FD-SOI and emerging 3D architectures such as GAA and CFET. Particular attention will be given to the engineering of optimized spacers, gate stacks, junction placement and epitaxial source/drain materials in order to minimize parasitic effects and enhance RF efficiency.
By comparing planar and 3D device platforms within a unified modelling and characterization framework, the thesis aims to provide technology guidelines for future generations of energy-efficient RF transistors targeting applications in 5G/6G communications, automotive radar and low-power IoT systems.