Effect of gamma-ray irradiation on ferroelectric, hafnia-based, non-volatile memory for use in extreme environments
The emergence of hafnia-based ferroelectric (FE) memories has opened a new paradigm for ultra-low-power edge computing. Hafnia is fully compatible with CMOS technology and is ultra low-power—three orders of magnitude less than other emerging memory technologies.
These advantages align with strategic applications in space, defense, medical, nuclear safety, and heavy-duty transport, where electronics face harsh radiation environments.
Imprint induces a shift of the Polarization-Voltage (P-V) curve along the voltage axis and is attributed to charge trapping/detrapping, domain pinning and charged defects. All may be accentuated under irradiation.
The project will use advanced photoelectron spectroscopy techniques including synchrotron radiation induced Hard X-ray photoelectron spectroscopy and complementary structural analysis including high-resolution electron microscopy, X-ray diffraction and near field microscopy. The experimental characterization will be accompanied by theoretical calculations to simulate the material response to irradiation
The work will be carried out in the framework of close collaboration between the CEA/Leti in Grenoble providing the samples, integrated devices and wafer scale characterization and the CEA/Iramis in Saclay for the fundamental analysis of the material properties, irradiation experiments and device scale characterizations.
3D interconnects for the design and fabrication of quantum processor units
To increase the performance of quantum computers, three-dimensional (3D) integration is now the key! Using technologies such as flip-chip bonding, multi-layer wiring or even through-silicon vias (TSV), 3D integration offers solutions to increase the number of qubits on a processor, reduce signal loss and cross-talk and even improve thermal management. All of these aspects are essential to continue scaling qubits to achieve fault-tolerant quantum computing.
Our team is developing 3D interconnect technologies (e.g. superconducting microbumps and TSV) for the next generation of quantum processors. This thesis will focus on the electrical and radiofrequency characterization of such interconnects and of the quantum devices integrated nearby to gain knowledge on how these 3D technological bricks may impact the quantum properties.
This position will bring you at the boundary between material, technological and physical challenges of quantum systems. You will work with teams from CEA-LETI and CEA-IRIG. As a PhD candidate, you will take part in the design and layout of test vehicles and in their fabrication. You will also lead the low temperature measurements of the fabricated samples, perform the associated analysis and write reports.
A theoretical framework for the task-based optimal design of Modular and Reconfigurable Serial Robots for rapid deployment
The innovations that gave rise to industrial robots date back to the sixties and seventies. They have enabled a massive deployment of industrial robots that transformed factory floors, at least in industrial sectors such as car manufacturing and other mass production lines.
However, such robots do not fit the requirements of other interesting applications that appeared and developed in fields such as in laboratory research, space robotics, medical robotics, automation in inspection and maintenance, agricultural robotics, service robotics and, of course, humanoids. A small number of these sectors have seen large-scale deployment and commercialization of robotic systems, with most others advancing slowly and incrementally to that goal.
This begs the following question: is it due to unsuitable hardware (insufficient physical capabilities to generate the required motions and forces); software capabilities (control systems, perception, decision support, learning, etc.); or a lack of new design paradigms capable to meet the needs of these applications (agile and scalable custom-design approaches)?
The unprecedented explosion of data science, machine learning and AI in all areas of science, technology and society may be seen as a compelling solution, and a radical transformation is taking shape (or is anticipated), with the promise of empowering the next generations of robots with AI (both predictive and generative). Therefore, research can tend to pay increasing attention to the software aspects (learning, decision support, coding etc.); perhaps to the detriment of more advanced physical capabilities (hardware) and new concepts (design paradigms). It is however clear that the cognitive aspects of robotics, including learning, control and decision support, are useful if and only if suitable physical embodiments are available to meet the needs of the various tasks that can be robotized, hence requiring adapted design methodologies and hardware.
The aim of this thesis is thus to focus on design paradigms and hardware, and in particular on the optimal design of rapidly-produced serial robots based on given families of standardized « modules » whose layout will be optimized according to the requirements of the tasks that cannot be performed by the industrial robots available on the market. The ambition is to answer the question of whether and how a paradigm shift may be possible for the design of robots, from being fixed-catalogue to rapidly available bespoke type.
The successful candidate will enrol at the « Ecole Doctorale Mathématiques, STIC » of Nantes Université (ED-MASTIC), and he or she will be hosted for three years in the CEA-LIST Interactive Robotics Unit under supervision of Dr Farzam Ranjbaran. Professors Yannick Aoustin (Nantes) and Clément Gosselin (Laval) will provide academic guidance and joint supervision for a successful completion of the thesis.
A follow-up to this thesis is strongly considered in the form of a one-year Post-Doctoral fellowship to which the candidate will be able to apply, upon successful completion of all the requirements of the PhD Degree. This Post-Doctoral fellowship will be hosted at the « Centre de recherche en robotique, vision et intelligence machine (CeRVIM) », Université Laval, Québec, Canada.
Artificial Intelligence for the Modeling and Topographic Analysis of Electronic Chips
The inspection of wafer surfaces is critical in microelectronics to detect defects affecting chip quality. Traditional methods, based on physical models, are limited in accuracy and computational efficiency. This thesis proposes using artificial intelligence (AI) to characterize and model wafer topography, leveraging optical interferometry techniques and advanced AI models.
The goal is to develop AI algorithms capable of predicting topographical defects (erosion, dishing) with high precision, using architectures such as convolutional neural networks (CNN), generative models, or hybrid approaches. The work will include optimizing models for fast inference and robust generalization while reducing manufacturing costs.
This project aligns with efforts to improve microfabrication processes, with potential applications in the semiconductor industry. The expected results will contribute to a better understanding of surface defects and the optimization of production processes.
Innovative techniques for evaluating critical steps and limiting factors for batteries formation
The battery manufacturing sector in Europe is currently experiencing strong growth. The electrical formation step that follows battery assembly and precedes delivery has received little academic attention, despite being crucial for battery performance (lifespan, internal resistance, defects, etc.). It is an essential time-consuming and costly step in the process (>30% of the cell manufacturing cost, and 25% of the equipment cost in a Gigafactory) that would greatly benefit from optimization.
In this thesis, we propose studying battery formation using innovative, complementary, operando non-intrusive techniques. The goal is to identify the limiting mechanisms of the electrolyte impregnation step (filling electrode pores) and of the initial charge. The candidate will implement experimental methods to monitor and analyze these mechanisms. He will also establish a methodology and protocols for studying these steps, combining electrochemical measurements with non-intrusive physical characterizations under operating conditions. The research will focus on optimizing formation time and quality control during this stage.
Differential phase contrast imaging based on quad-pixel image sensor
Biopharmaceutical production is booming and consists of using cells to produce molecules of interest. To achieve this, monitoring the culture and the state of the cells is essential. Quantitative phase imaging by holography is a label-free optical method that has already demonstrated its ability to measure the concentration and viability of cultured cells. However, implementing this technique in a bioreactor faces several challenges related to the high cell density. It is therefore necessary to develop new quantitative phase imaging methods, such as differential phase contrast imaging.
The objective of the PhD is to develop this technique using a specific image sensor for which a prototype has been designed at CEA-LETI. The PhD candidate will use this new sensor and develop the reconstruction and image-processing algorithms. They will also identify the limitations of the current prototype and define the specifications for a second prototype that will be developed at CEA-LETI. Finally, they will consider the design of an inline probe to be immersed in the bioreactor.
Designing a hybrid CPU-GPU estimator for neutron transport: Advancing eco-efficient Monte Carlo simulations
Digital twins incorporating Monte Carlo simulation models are currently being developed for the design, operation, and decommissioning of nuclear facilities. These twins are capable of predicting physical quantities such as particle fluxes, gamma/neutron heating, and dose equivalent rates. However, the Monte Carlo method presents a major drawback: high computational time to achieve acceptable variance levels.
To enhance simulation efficiency, the eTLE estimator has been developed and integrated into the TRIPOLI-4® Monte Carlo code. Compared to the conventional TLE (Track Length Estimator), eTLE offers lower theoretical variance, particularly in highly absorbing media, by contributing to the detector response even when particles do not physically reach it. Nevertheless, its computational cost remains significant, especially when evaluating multiple detectors.
Two recent PhD works have proposed variants to overcome this limitation. The Forced Detection eTLE- (Guadagni, EPJ Plus 2021) employs preferential sampling that directs pseudo-particles toward the detector at each collision. It is particularly effective for small detectors and configurations with moderate shielding, especially for fast neutrons. The Split Exponential TLE (Hutinet & Antonsanti, EPJ Web 2024) is based on an asynchronous GPU approach, offloading straight-line particle transport to the graphics processor. Through multiple sampling, it maximizes GPU utilization and enables more efficient exploration of phase space.
The proposed thesis aims to combine these two approaches into a hybrid estimator named seTLE-DF. This new estimator could be used either directly or to generate importance maps without relying on auxiliary deterministic calculations. Its implementation will require dedicated GPU developments, particularly to optimize the geometry library and memory management in complex geometries.
This research topic aligns with green computing objectives, aiming to reduce the carbon footprint of high-performance computing. It relies on a hybrid CPU-GPU strategy, avoiding full porting of the Monte Carlo code to GPU. Solutions such as half-precision formats will be considered, and an energy impact assessment will be conducted before and after implementation. The future PhD student will be welcomed with the IRESNE Institute (CEA Cadarache)and will acquire strong expertise in neutron transport simulation, facilitating integration into major research institutions or companies within the nuclear sector.
Design artificial intelligence tools for tracking Fission Product release out of nuclear fuel
The Laboratory for the Analysis of Radionuclide Migration (LAMIR), part of the Institute for Research on Nuclear Systems (IRESNE) at CEA Cadarache, has developed a set of advanced measurement methods to characterize the release of fission products from nuclear fuel during thermal transients. Among these innovative tools is an operando in situ imaging system that enables real-time observation of these phenomena. The large amount of data generated by these experiments requires dedicated digital processing techniques that account for both the specificities of nuclear instrumentation and the underlying physical mechanisms.
The goal of this PhD project is to develop an optimized data processing approach based on state-of-the-art Artificial Intelligence (AI) methods.
In the first phase, the focus will be on processing thermal sequence images to detect and analyze material movements, aiming to identify an optimal image-processing strategy defined by rigorous quantitative criteria.
In the second phase, the methodology will be extended to all experimental data collected during a thermal sequence. The long-term objective is to create a real-time diagnostic tool capable of supporting experiment monitoring and interpretation.
This PhD will be carried out within a collaborative framework between LAMIR, which has recognized expertise in nuclear fuel behavior analysis and imaging, and the Institut Fresnel in Marseille, known for its strong background in image analysis and artificial intelligence.
The candidate will benefit from a multidisciplinary and stimulating research environment, with opportunities to present and publish their work at national and international conferences and in peer-reviewed journals.
Development of a dosimeter based on the capture of xenon in a zeolite
Reactor dosimetry makes possible to characterize the neutron spectrum (neutron energy distribution) and to determine the neutron fluence received during irradiation for monitoring the embrittlement of materials. This technique relies on analyzing the radioactivity of irradiated dosimeters, made of pure metals or alloys of known compositions, some isotopes of which undergo activation or fission reactions.
There are numerous dosimeters sensitive to 2 MeV, a few between 1 MeV and 2 MeV, but Zr is the only one suitable for the energy range between 1 keV and 1 MeV. Moreover, few dosimeters respond with a threshold close to 1 MeV in moderate-flux R&D reactors. The only one practically usable, Rh, has a half-life < 1 h, and its measurement relies solely on highly self-absorbed X-rays, requiring very thin dosimeters and complicating measurements. There is therefore a real need to develop a dosimeter capable of responding between 1 keV and 1 MeV.
In this context, Xe not only exhibits an interesting reaction already identified between 1 keV and 1 MeV, but also has two reactions close to 1 MeV producing two nuclides with half-lives of about ten days, well suited to the irradiation cycles of the upcoming high-flux experimental reactor at CEA: the Jules Horowitz Reactor (JHR).
The main idea of this thesis topic would be to use adsorbent materials to trap a sufficient mass of Xe in a reduced volume. Some commercial zeolites can now trap up to 30% by weight of Xe when exposed to only 1 bar of Xe at room temperature.
The thesis will consist of producing a Xe dosimeter trapped on a zeolite at CNRS MADIREL (frequent trips to the Saint Jérôme campus in Marseille in the first year) as well as a simplified Xe-filled chamber manufactured in in the workshops of our laboratory. The common irradiation of a dosimeter and a chamber in a reactor such as CABRI in Cadarache will allow the evaluation of the self-absorption factors by the zeolite of the gamma lines emitted by the isotopes of interest, verification of their measurability with the MADERE platform of our laboratory, as well as assessment of the ageing of zeolites under strong neutron irradiation. The dosimeter will then be tested at higher neutron flux, for example in the TRIGA reactor at JSI (one-week trip to Slovenia to be expected), through the uninterrupted CEA-JSI collaboration since 2008, in order to qualify this dosimeter for JHR.
By acquiring expertise in the field of nuclear measurement, the future PhD graduate will be well prepared for professional integration into major French and international research organizations, or in nuclear companies.
Proximal primal-dual method for joint estimation of the object and of unknown acquisition parameters in Computed Tomography.
As part of the sustainable and safe use of nuclear energy in the transition to a carbon-free energy future, the Jules Horowitz research reactor, currently under construction at the CEA Cadarache site, is a key tool for studying the behaviour of materials under irradiation. A tomographic imaging system will be exploited in support of experimental measures to obtain real-time images of sample degradation. This imaging system has extraordinary characteristics due to its geometry and to the size of the objects to be characterized. As a result, some acquisition parameters, which are essential to obtain a sufficient image reconstruction quality, are not known with precision. This can lead to a significant degradation of the final image.
The objective of this PhD thesis is to propose methods for the joint estimation of the object under study and of the unknown acquisition parameters. These methods will be based on modern convex optimization tools. This thesis will also explore machine learning methods in order to automate and optimize the choice of hyperparameters for the problem.
The thesis will be carried out in collaboration between the Marseille Institute of Mathematics (I2M CNRS UMR 7373, Aix-Marseille University, Saint Charles campus) and the Nuclear Measurement Laboratory of the IRESNE institute of the French Alternative Energies and Atomic Energy Commission (CEA Cadarache, Saint Paul les Durance). The doctoral student will work in a stimulating research environment focused on strategic questions related to non-destructive testing. He or she will also have the opportunity to promote his or her research work in France and abroad.