Strain field imaging in semiconductors: from materials to devices
This subject addresses the visualization and quantification of deformation fields in semiconductor materials, using synchrotron radiation techniques. The control of the deformation is fundamental to optimize the electronic transport, mechanical and thermal properties.
In a dual technique approach we will combine the determination of the local deviatoric strain tensor by scanning the sample under a polychromatic nano beam (µLaue) and a monochromatic full field imaging with a larger beam (dark field x ray microscopy, DFXM).
New developments of the analysis will be focused on 1/ the improvement of the accuracy and speed of the quantitative strain field determination, 2/ the analysis of strain gradient distributions in the materials, and 3/ the determination of the dynamic strain field in piezoelectric materials through stroboscopic measurements. To illustrate these points, three scientific cases corresponding to relevant microelectronic materials of increasing complexity will be studied:
1- Static strain fields surrounding metallic contacts, such as high-density through silicon vias (TSV) in CMOS technology.
2- Strain gradients in Ge/GeSn complex heteroepitaxial structures with compositional variations along the growth direction.
3- Dynamical strain in LiNbO3 surface acoustic wave resonators with resonance frequency in the MHz range bulk
Establishing this approach will mean moving a step forward towards more efficient microelectronics and strain engineering.
Advanced functions for monitoring power transistors (towards greater reliability and increased lifespan of power converters for energy)
In order to increase the power of electronic systems, a common approach is to parallelize components within modules. However, this parallelization is complicated by the dispersion of transistor parameters, both initial and post-aging. Fast switching of Wide Bandgap (WBG) semiconductors components often requires slowdowns to avoid over-oscillation and destruction.
An intelligent driving scheme, including adjusted control, control of internal parameters of circuits and devices, as well as a feedback loop, could improve reliability, service life and reduce the risk of breakage.
The objectives of the thesis will be to develop, study and analyze the performance of control and piloting functions of power components, in silicon carbide (SiC) or gallium nitride (GaN), which could ultimately be implemented in a dedicated integrated circuit (ASIC type).
This thesis subject aims to solve critical problems in the parallelization of power components, thus contributing to eco-innovation by increasing the lifespan of power modules.
The design of integrated circuits requires, at the end of the chain, circuit editing and failure analysis tools. One of these tools is the probing of electrical potential levels using an electron beam available in a SEM (Scanning Electron Microscope) to determine the electrical signal present in an area of the circuit, which may be a metal level or a transistor. This electronic probing technique was widely used in the 90s, and then partially abandoned despite a few recurrent publications on the technique. In recent years, this technique has been revived by using the backside of the component, probing via the silicon substrate and accessing the active areas of the component.
These debugging and failure analysis tools are also tools for attacking integrated circuits. This thesis topic falls within the scope of hardware cybersecurity and so-called invasive attacks. The PhD student will implement this electron beam probing technique on commercial SEMs and under conditions of use specific to cybersecurity. Various techniques will be considered to improve the probed signals and their use.
Development of a microfluidic bioanalytical platform to quantify the cellular bio-distribution of a drug
A drug's mode of action and efficacy are correlated not only with its ability to accumulate in the targeted pathological tissues, i.e. its tissue bio-distribution, but also with its ability to specifically reach its molecular target within cells. Non-specific accumulation of a drug in these cells can be the cause of undesired effects, such as side effects during chemotherapy. In other words, assessing a drug's efficacy, specificity and absence of toxicity requires precise, quantitative determination of its cellular bio-distribution. Antibody-drug conjugates (ADCs) have become an indispensable tool in oncology, enabling vectorized therapy to preferentially target a subset of tumor cells expressing the antigen recognized by the antibody.
These ADCs target specific tumor cells expressing a particular antigen, thus limiting toxicity to healthy tissue. Radioactive labeling of drugs (3H, 14C) is a key method for quantifying their accumulation in tumor and non-tumor cells, in order to assess targeting accuracy and avoid undesirable side effects. However, the detection of low-level tritium emissions requires new technological solutions. The project proposes the development of an innovative microfluidic platform to detect and quantify these isotopes in single cells. This approach will enable us to better document ADC distribution in heterogeneous tissues and refine therapeutic strategies.
A revolution in intervention in complex environments: AI and Digital twins in synergy for innovative and effective solutions.
Scientific Context
The operation of complex equipment, particularly in the nuclear sector, relies on quick and secure access to heterogeneous data. Advances in generative AI, combined with Digital Twins (DT), offer innovative solutions to enhance human-system interactions. However, integrating these technologies into critical environments requires tailored approaches to ensure intuitiveness, security, and efficiency.
Proposed Work
This thesis aims to develop a generative AI architecture enriched with domain-specific data and accessible via mixed reality, enabling a glovebox operator to ask natural language questions. The proposed work includes:
A review of the state-of-the-art on Retrieval-Augmented Generation (RAG), ASR/TTS technologies, and Digital Twins.
The development and integration of a chatbot for nuclear operations.
The evaluation of human-AI interactions and the definition of efficiency and adoption metrics.
Expected Outcomes
The project aims to enhance safety and productivity through optimized interactions and to propose guidelines for the adoption of such systems in critical environments.
Optimization by Artificial Intelligence of In Situ Characterization of Pure Beta Radionuclides in Complex Environments
Before, during, and after... the characterization of the radiological state is essential at all stages of the decommissioning scenario of a nuclear facility. Can we intervene directly on-site, or is teleoperation necessary? Has the contamination of a given area been completely eliminated? How should we categorize a particular nuclear waste to optimize its future management?
In-situ non-destructive nuclear measurements aim to evaluate the radiological state of processes and equipment in real time, while meeting criteria of efficiency, safety, flexibility, and reliability, and reducing costs through rapid, precise, and non-invasive analyses. While characterization techniques for gamma emitters are well mastered, those for pure beta emitters remain a significant challenge due to the low range of beta radiation in matter and the ambient gamma noise, which makes in-situ detection particularly complex.
The integration of artificial intelligence (AI) tools, such as machine learning or deep learning, in this field opens new perspectives. These technologies enable the automation of the analysis of large amounts of data while extracting complex information that is often difficult to interpret manually, particularly for deconvoluting continuous beta radiation spectra. Initial results obtained in the framework of L. Fleres' thesis have shown that AI can effectively predict and quantify the beta-emitting radionuclides present in a mixture. Although promising, this approach, tested in laboratory conditions, still needs to be qualified in real-world field conditions.
The proposed thesis aims to continue and refine these developments. It will involve integrating new algorithms, exploring various neural network architectures, and enriching learning databases to improve the performance of current systems for the in-situ characterization of beta emitters. This will include scenarios where the beta/gamma signal-to-noise ratio is degraded, as well as the detection of low levels of activity in the presence of natural radioactivity. Other research avenues will include the detection of low-energy radionuclides and the adaptation of deconvolution tools for large-surface detectors.
The characterization methodology developed at the end of the project will have strong potential for industrial valorization, particularly in the fields of decontamination and decommissioning. The doctoral candidate will join a team with extensive experience in the implementation of non-destructive radiological characterization techniques and methods in-situ and will have the opportunity to evaluate the proposed solutions on some of the largest decommissioning projects in the world.
Desired Profile: The ideal candidate holds a degree from an engineering school or a Master's (M2) with solid knowledge of nuclear measurement, particularly regarding the physical phenomena related to the interactions of ionizing radiation with matter. Skills in statistical data processing methods and programming (Python, C++) would also be appreciated.
Mesoscopic simulations and development of simplified models for the mechanical behaviour of irradiated concrete
In nuclear power plants, the concrete biological shield serves as a support for the reactor vessel and as a protective shield against radiation. Over the long term, prolonged exposure to neutron radiation can cause the concrete aggregates to expand through amorphisation, leading to micro-cracking and degradation of its mechanical properties. This is an important issue in studies aimed at extending the life of power plants. At the mesoscale, these phenomena can be modelled by separating the behaviour of the aggregates, the cementitious matrix and the interfacial transition zones. However, it is difficult to describe the initiation and propagation of microcracks in such complex heterogeneous multi-cracked systems. The aim of this thesis, carried out as part of a Franco-Czech ANR project, is to develop a high-performance numerical simulation tool for analysing the effects of neutron irradiation on concrete at the mesoscopic scale. A coupled thermo-hydro-mechanical approach will be used in which the behaviour of the matrix will take into account shrinkage, creep and micro-cracking. The simulations will be validated using experimental data obtained on tested samples, and the numerical tool will then be used to estimate the impact of various factors on the behaviour and performance of concrete subjected to neutron irradiation.
This research project is aimed at a PhD student wishing to develop their skills in materials science, with a strong focus on multiphysical and multiscale modelling and numerical simulations.
Cohesive powder simulation: link between atomic and grain scale
Nuclear fuel is produced through a powder metallurgy process that involves several stages of the granular medium preparation (grinding, mixing, pressing and sintering). The powders used during these stages exhibit strong cohesion between the grains, making their flow behavior complex. Predicting powder behavior is a critical industrial challenge to quickly adapt to raw material changes, optimize product quality, and enhance production rates.
This thesis aims to establish a link between the properties of powders and their behavior during flow and pressing. Grain cohesion is a key factor that influences both the flow and densification of granular materials. This cohesion is governed by several interparticle forces, such as van der Waals forces, capillary interactions, and electrostatic forces. Understanding these interactions at the atomic scale is essential for accurately predicting and modeling powder behavior. The thesis seeks to address two central questions: How do the surface properties of grains at the atomic level influence the cohesive forces at the grain scale? And how can we scale up from the atomic level to the grain scale to simulate powders more realistically?
Multi-scale simulation approaches are crucial for bridging the gap between microscopic phenomena and the macroscopic behavior of granular materials. Current Discrete Element Method (DEM) simulations rarely incorporate fundamental interactions, such as van der Waals forces, electrostatic forces, and capillary effects, into their contact models. Recent research (1) (2) has explored the impact of cohesion using a simplified approach, treating it as an attractive force or cohesive energy. Simulation methods like Molecular Dynamics (MD) or Coarse-graining enable the modeling of material behavior at finer scales, based on these local structural and chemical properties. A deeper understanding of cohesion at small scales will enhance the predictive capabilities of DEM simulations and clarify the relationship between powder properties and their overall behavior.The main goal of this thesis is to better understand the relationships between atomic-scale interactions and grain-scale cohesion and to assess the consequences for simulations of powder pressing and flow.
The primary goal of this thesis is to make connections between the atomic-scale interactions and grain-scale cohesion and to simulate the powder flow and compaction processes.
One of the main challenges in this project is the development of DEM contact laws that incorporate complex atomic-scale interactions. This will require close collaboration between experts in atomic-level simulations and those working on DEM modeling. Additionally, validating these models through experimental comparisons is essential to ensure their accuracy and relevance for industrial applications.
The PhD candidate will be based at the IRESNE Institute (CEA-Cadarache) within the Laboratory of Numerical Methods and Physical Components on the PLEIADES platform, part of the Department of Fuel Studies. They will collaborate with the Fuel Behavior Modeling Laboratory and will have access to state-of-the-art modeling and simulation tools, as well as a collaborative environment with the Mechanics and Civil Engineering Laboratory at the University of Montpellier.
Bibliography
1. Sonzogni, Max. Modélisation du calandrage des électrodes Li-ion en tant que matériau granulaire cohésif : des propriétés des grains aux performances de l'électrode. s.l. : Thèse, 2023.
2. Tran, Trieu-Duy. Cohesive strength and bonding structure of agglomerates composed. 2023.
Complex 3D structuring based on DNA origami
The rapid evolution of new technologies, such as autonomous cars and renewable energy, requires the development of increasingly complex structures. To achieve this, many surface patterning techniques are available today. In microelectronics, optical lithography is the standard method for creating micro- and nanometric patterns. However, it remains limited in terms of the diversity of shapes it can produce.
In recent years, a promising approach has been developed within the laboratories of CBS (INSERM in Montpellier) and the CEA Leti (Grenoble): DNA origami assembly. This technology exploits the self-assembly properties of the DNA origami polymer chain. The assembly of nanometric DNA origami ultimately forms micrometric structures. The aim of this PhD is to explore new perspectives by combining 2D and 3D origami to create novel structures. These patterns could be of great interest for applications in fields such as optics or energy.