ALD materials for FE and AFE capacitances
Ultrathin HfO2-based materials are regarded as promising candidates for embedded non-volatile memory (eNVM) and logic devices. The CEA-LETI has a leadership position in the field of BEOL-FeRAM memories ultra-low consumption (<100fj/bit) at low voltage (<1V). In this context, the developments expected in this thesis aim to evaluate the impact of HfO2-based ferroelectric FE and antiferroelectric AFE layers (10 to 4 nm fabricated by Atomic Layer Deposition ALD) on the FeRAM properties and performances.
In particular, the subject will permit a deep understanding of the crystallographic phases governing the FE/AFE properties using advanced measurements techniques offered by the CEA-LETI nano-characterization platform (physico-chemical, structural and microscopy analysis, electrical measurements). Several integration solutions for ferroelectric capacitances FeCAPs using ALD FE/AFE layers will be studied including doping, interface layers, sequential fabrication w/wo air break…
Thus, the developments based on FeCAPs stack fabricated using 300mm ALD deposition tool aspires to explore the following items:
1-Doping incorporation in FE/AFE layers (La, Y…)
2-Engineering of the interface between FE/AFE layers and top/bottom electrode
3-Plasma in-situ treatment of bottom electrode surface
4-Sequential deposition with and without air break
[1] S. Martin et al. – IEDM 2024
[2] Appl. Phys. Lett. 124, 243508 (2024)
Thermomechanical behaviour at high temperature of an irradiated nuclear ceramic
This thesis is part of the studies on pellet-cladding interactions in nuclear fuel rods used in NPP. The operator must ensure and demonstrate the integrity of rods in any situations. The mechanical stresses on the clad, the first safety barrier, are linked to the viscoplastic properties of the fuel. It is therefore necessary to know these behaviors and their evolution in operation.
The topic proposed will focuse on the characterization, in hot lab, of an irradiated fuel. One of the main difficulties is that the irradiated fuels in a reactor are multi-cracked, which makes their mechanical characterization particularly complex. However, an ongoing thesis (2022-25) has reached different steps: (i) the design of a specific thermomechanical testing machine, (ii) the partial qualification of this device, (iii) the implementation of tools and cracked sample extraction method, (iv) and a whole system model (digital twin).
The thesis will be the continuation of this work and will be built in four stages on three experimental platforms available at the CEA:
1. Getting the knowledge and improving existing digital and experimental tools,
2. Implementation of the device in hot-cell on an existing furnace,
3. Thermomechanical testing on irradiated fuel, a world first time in these conditions.
The tests will require dedicated post-processing based on simulation-experiments comparisons. Once the experimental base is sufficiently developed and interpreted, it will then be possible to confirm or revise the irradiated fuel behaviour laws. A link with the microstructure of materials could be addressed.
Throughout these stages, the PhD student will draw on skills and expertise of laboratories of the Fuel Research Department (IRESNE Institute, CEA Cadarache) and on a academic collaboration. This thesis also fits into the framework of the European project OPERA HPC and is a major issue.
The PhD student should have a strong taste for the experimental approach and some facilities for the use of digital tools. Knowledge of materials science is the minimum required. During the three years, the PhD student will improve his multiphysical skills in experimental device design and high-temperature material behavior, as well as in numerical simulation, which will facilitate his professional integration.
Bottom-up study of Ionic Transport in Unsaturated Hierarchical Nanoporous Materials : application to cement-based materials
Ion transport is critical in determining the durability of cement-based materials and, therefore, the extension of service life of concrete (infra)structures. Transport phenomena determine the containment capacity of concrete, which is crucial in the design and asset management of concrete infrastructures for energy production. Under most service conditions, concrete exists in unsaturated conditions. Anomalous transport has been associated with cement-based materials, and the reasons behind such deviations from the expected behavior of other porous materials may stem from nanoscale processes.
Research efforts have aimed to correlating material composition and microstructure to transport properties and durability. However, to date, the majority of predictive modeling of durability does not explicitly account for nanoscale processes, which are fundamental in determining transport properties. Recent advances have been made in quantifying the behavior of confined water in various phases present in cement systems. Calcium silicate hydrates (C-S-H) are the main hydrated phase in cement-based materials and present nanopores in the micro and mesopore range. The effects of desaturation remain however to be fully worked out. A fundamental understanding of transport processes requires a multiscale framework in which information from the molecular scale reverberates across other relevant scales (in particular, the mesoscale associated with C-S-H gel porosity (~nm), capillary porosity, and interfacial transition zone (~µm) up to the macroscopic scale of industrial application in cement-based materials).
The goal of this PhD work is to evaluate the ionic transport of chlorides, a critical species for the durability of concrete, under non-saturated conditions by combining small-scale simulations, multiscale modelling and experimentation in a bottom-up approach. The work will focus on the C-S-H. The project aims to characterize the effects of desaturation on the nanoscale processes driving transport of chlorides.
PRObablistic on-edge learning for SPINtronic-based neuromorphic systems
The hired joint UGA – KIT PHD candidate should be able to cover the work of the workpackage 1 and 2. He/she will also participate to technical meetings and have a good understanding on how the tasks of the other technical workpackages are executed, mainly by the partners with internal effort. As a whole, the PHD candidate will develop and optimize compact Computing in Memory architectures, provide high level models for further integration in large scale designs, perform validation of all proofs of concepts of new architectural implementations. He/she will be involved also in the design of algorithmic implementations of Bayesian Neural Networks adapted to the architecture. More in details, he/she will work on the following directions:
Design and optimization of the probabilistic neural networks, will be executed mostly in SPINTEC Laboratory in Grenoble, that will include:
1. full design stack of hardware accelerator without selector transistor for frequent Read and Write operations.
2. Design and validate an innovative architectural approach able to compensate for sneaky paths phenomena.
3. High-level modeling of the full crossbar architecture that includes the stochastic component.
4. Propose a full simulation and validation flow scalable to scaled to realistic architecture size and parameters that implement Bayesian tasks.
5. Perform Delay, power consumption and area overhead figures of merit
Thermo-chemo-mechanical modeling of sintering : effect of atmosphere and the differential densification on pellet shrinkage
Uranium dioxide (UO2) fuels used in nuclear power plants are ceramics, for which solid-phase sintering is a key manufacturing step. The sintering stage involves heat treatment under controlled partial O2 pressure that induces coarsening of UO2 grain and then consolidation and densification of the material. Densification induces macroscopic shrinkage of the pellet. If the compact (powder obtained by pressing, manufacturing step before sintering) is highly heterogeneous density, a difference in densification within the pellet may occur, leading to differential shrinkage and the appearance of defects.
The PhD thesis aims at developing a Thermo-chemo-mechanical modeling of sintering to simulate the impact of the gas composition and properties on the pellet densification. This scale will enable us to take into account not only the density gradients resulting from pressing, but also the oxygen diffusion kinetics that have a local impact on the densification rate, which in turn impacts the transport process. Therefore, a multiphysics coupling phenomenon has to be modelled and simulated.
This thesis will be conducted within the MISTRAL joint laboratory (Aix-Marseille Université/CNRS/Centrale Marseille CEA-Cadarache IRESNE institute). The PhD student will leverage his results through publications and participation in conferences and will have gained strong skills and expertise in a wide range of academic and industrial sectors.
Modeling of complexation equilibria of actinides in nitric medium. Application to the PUREX process
The PAREX+ code is a major tool in the field of separation chemistry. It allows for the modelling and simulation of separation processes base on solvent extraction. In this code, the distribution of interest species between the aqueous and organic phases is calculated at every point in the process, both in steady and transitory states. The aim of this thesis is to improve this distribution model. To achieve this, a better understanding of the phenomena involved in the organic and aqueous phases is necessary, as well as a new approach to incorporate them into the model. This thesis thus combines experimental work and modeling. The student will join a supervisory team composed of experts in separation chemistry and modeling. His work will be valued through the publication of papers and participation in international conferences. At the end of this thesis, the student will have solid knowledge in the field of solvent extraction and its modeling, which he can leverage with industry or research organizations in the nuclear field or in other areas of separation chemistry (separation of rare earths or hydrometallurgy).
Innovative modeling for multiphysics simulations with uncertainty estimates applied to sodium-cooled fast reactors
Multiphysics modeling is crucial for nuclear reactor analysis, yet uncertainty propagation across different physical domains—such as thermal, mechanical, and neutronic behavior—remains underexplored due to its complexity. This PhD project aims to address this challenge by developing innovative methods for integrating uncertainty quantification into multiphysics models.
The key objective is to propose optimal modeling approaches tailored to different precision requirements. The project will explore advanced techniques such as reduced-order modeling and polynomial chaos expansion to identify which input parameters most significantly impact reactor system outputs. A key aspect of the research is the comparison between "high-fidelity" models, developed using the CEA reference simulation tools, and "best-estimate" models designed for industrial use. This comparative analysis will highlight how these errors propagate through different models and simulation approaches.
The models will be validated using experimental data from SEFOR, a sodium-cooled fast reactor. These experiments provide valuable benchmarks for testing multiphysics models in realistic reactor conditions. This research directly addresses the growing need for reliable, efficient modeling tools in the nuclear industry, aiming to improve reactor safety and performance.
The candidate will work in a dynamic environment at the CEA, benefiting from access to advanced simulation resources and opportunities for collaboration with other researchers and PhD students. The project offers the possibility of presenting results at national and international conferences, with strong career prospects in nuclear reactor design, safety analysis, and advanced simulation.
oxygen ordering in zirconium: mechanisms, kinetics and associated mechanical properties
The aim of this work is to study the properties of binary zirconium-oxygen (Zr-O) alloys, particularly in the context of nuclear applications. Traditionally, oxygen is considered to be in solid solution in the zirconium matrix, without the formation of ordered compounds such as Zr6O or Zr3O. However, recent studies suggest that at temperatures below 600°C, ordered compounds can form, affecting the solubility limit of oxygen. These compounds, observed after heat treatments, could modify the mechanical properties of Zr-O alloys, particularly at room temperature and up to 350°C. The proposed thesis seeks to understand these mechanisms through X-ray diffraction and electron microscopy experiments, in order to study the arrangement of oxygen, the thermal stability of the compounds and their impact on plastic deformation. The aim is to optimise the use of these alloys in nuclear reactors.
Quantification and Optimization of the Mechanical State of Nb3Sn Superconductors during the Heat Treatment
In agreement with the CERN’s advertised will for the implementation of a super-collider, FCC type, high field superconducting electromagnets, based on Nb3Sn, are being developed. In the framework of the HFM (High Field Magnets) European collaboration, the LEAS at CEA Paris-Saclay is designing, manufacturing, and testing superconducting magnet demonstrators generating up to 16 T. Nb3Sn conductors require a heat treatment at 650 °C. During this heat treatment, several physico-chemical phenomena lead to the formation of the Nb3Sn superconducting phase. These phenomena induce a mechanical state impacting the superconducting properties of the material. A study of the different phenomena inducing dimensional changes inside the conductors would allow estimating the stresses inside the Nb3Sn superconducting phase following the heat treatment. The goal of this thesis is to study, using modeling and experiments, the thermomechanical state of the conductors during the heat treatment in order to estimate the internal stresses and their impact on the superconducting performances. The results will allow the improvement of the Nb3Sn superconducting properties in view of the production of high field magnets for future accelerators.
Digital reconstruction of an industrial tank for the improvement of real-time monitoring instrumentation
In the context of industrial digitalization and real-time monitoring, accessing 3D fields (velocity, viscosity, turbulence, concentration, etc.) in real time can be crucial, as local sensor networks are sometimes insufficient to provide a comprehensive view of the system's dynamics. This PhD project aims to investigate a methodology for the real-time reconstruction of fields within an instrumented industrial tank equipped with a mixing system. The proposed approach relies on finite element modeling of the relevant physics within the tank (e.g., fluid dynamics, thermal processes) and model reduction techniques such as physics-based Machine Learning (virtual sensor approach). A key focus of this thesis will also be the development of the tank instrumentation and its associated acquisition chain, both to validate the models and to generate a database for applying the proposed methodology.