Investigation and Modeling of Ferroelectric and Antiferroelectric Domain Dynamics in HfO2-Based Capacitors
The proposed PhD work lies within the exploration of new supercapacitor and hybrid energy storage technologies, aiming to combine miniaturization, high power density, and CMOS process compatibility. The hosting laboratory (LTEI/DCOS/LCRE) has recognized expertise in thin-film integration and dielectric material engineering, offering unique opportunities to investigate ferroelectric (FE) and antiferroelectric (AFE) behaviors in doped hafnium oxide (HfO2).
The thesis will focus on the experimental investigation and physical modeling of thin-film HfO2-based capacitors, intentionally doped to exhibit ferroelectric or antiferroelectric properties depending on the composition and deposition conditions (for instance, through ZrO2 or SiO2 doping). Such materials are particularly attractive for realizing devices that combine non-volatile memory and energy storage functions on a single CMOS-compatible platform, enabling ultra-low-power autonomous systems such as edge computing architectures, environmental sensors, and smart connected objects.
The research will involve the fabrication and characterization of metal–insulator–metal (MIM) capacitors based on doped HfO2 integrated on silicon substrates. Systematic electrical measurements—including current–voltage (I–V) and polarization–electric field (P–E) characterizations—will be carried out under various frequencies, amplitudes, and cycling conditions to investigate the relaxation mechanisms of FE and AFE domains. Analysis of minor hysteresis loops will provide access to the distribution of activation energies and enable the modeling of domain relaxation dynamics. A physical model will be developed or refined to describe FE/AFE transitions under cyclic electrical excitation, incorporating effects such as charge trapping, mechanical stress, and domain nucleation kinetics.
The overall objective is to optimize the recoverable energy density and the energy conversion efficiency of these capacitors, while establishing design guidelines for compact, efficient, and silicon-integrable energy storage devices. The insights gained from this work will contribute to a deeper understanding of the dynamic mechanisms governing FE/AFE behavior in doped HfO2, with potential impact on ferroelectric memories, energy-harvesting devices, and low-power neuromorphic architectures.
Fabrication of Metasurfaces by Self-Assembly of Block Copolymers
Block copolymers (BCP) are an industrial technology in full expansion, offering promising perspectives for material nanostructuring. These polymers, composed of chemically distinct block chains, self-assemble to form ordered structures at the nanometric scale. However, their current use is limited to specific nanostructuring per product (1 product = 1 nanostructuring), thus restricting their application potential.
This PhD proposes to develop an innovative method to create multiple patterns in a single BCP self-assembly step using a mixture of two products. The student will also focus on controlling the localization of these patterns using chemoepitaxy, a technique combining chemical and morphological guidance to precisely control the position of patterns at the micrometric and nanometric scales.
The work will proceed in several steps: understanding the mechanisms of mixed block copolymers, developing functionalized substrates for chemoepitaxy using advanced lithography techniques, and conducting BCP self-assembly experiments on these substrates. The resulting structures will be analyzed using the metrology equipment available at CEA-Leti.
The targeted applications include the creation of nanostructures capable of interacting with light, reducing diffraction, and controlling polarization. The expected results include demonstrating the ability to generate multiple types of patterns in a single self-assembly step, with precise control over their position and dimensions.
Development and validation of surface haptics machine learning algorithms for touch and dexterity assessment in neurodevelopmental disorders
The aim of this PhD thesis is to develop new clinical assessment methods using surface haptics technologies, developed at CEA List, and machine learning algorithms for testing and monitoring tactile-motor integration. In particular, the thesis will investigate and validate the development of a multimodal analytics pipeline that converts surface haptics signals and dexterity exercises inputs (i.e. tactile stimulation events, finger kinematics, contact forces, and millisecond timing) into reliable, interpretable biomarkers of tactile perception and sensorimotor coupling, and then classify normative versus atypical integration patterns with clinical fidelity for assessment.
Expected results: a novel technology and models for the rapid and feasible measurement of tactile-motor deficits in clinical setting, with an initial validation in different neurodevelopmental disorders (i.e. first-episode psychosis, autism spectrum disorder, and dyspraxia). The methods developed and data collected will provide:
(1) an open, versioned feature library for tactile–motor assessment;
(2) classifiers with predefined operating points (sensitivity/specificity);
(3) and an on-device/edge-ready pipeline, i.e. able to run locally on a typical tablet hardware whilst meeting constraints on latency, computing, and data privacy. Success will be measured by reproducibility of features, clinically meaningful effect sizes, and interpretable decision logic that maps back to known neurophysiology rather than artefacts.
Physical-attack-assisted cryptanalysis for error-correcting code-based schemes
The security assessment of post-quantum cryptography, from the perspective of physical attacks, has been extensively studied in the literature, particularly with regard to the ML-KEM and ML-DSA standards, which are based on Euclidean lattices. Furthermore, in March 2025, the HQC scheme, based on error-correcting codes, was standardized as an alternative key encapsulation mechanism to ML-KEM. Recently, Soft-Analytical Side-Channel Attacks (SASCA) have been used on a wide variety of algorithms to combine information related to intermediate variables in order to trace back to the secret, providing a form of “correction” to the uncertainty associated with profiled attacks. SASCA is based on probabilistic models called “factor graphs,” to which a “belief propagation” algorithm is applied. In the case of attacks on post-quantum cryptosystems, it is theoretically possible to use the underlying mathematical structure to process the output of a SASCA attack in the form of cryptanalysis. This has been demonstrated, for example, on ML-KEM. The objective of this thesis is to develop a methodology and the necessary tools for cryptanalysis and residual complexity calculation for cryptography based on error-correcting codes. These tools will need to take into account information (“hints”) obtained from a physical attack. A second part of the thesis will be to study the impact that this type of tool can have on the design of countermeasures.
Bayesian Neural Inference Using Ferroelectric Memory Transistors
An increasing number of safety-critical systems now rely on artificial intelligence functions that must operate under strict energy constraints and in environments characterized by data scarcity and high uncertainty. However, conventional deterministic AI approaches provide only point estimates and lack principled uncertainty quantification, which can lead to unreliable or unsafe decisions in real-world deployment.
This PhD is positioned within the emerging field of Bayesian electronics, which aims to implement probabilistic inference directly in hardware by leveraging the intrinsic stochasticity of nanoscale devices to represent and manipulate probability distributions. While memristive devices have previously been explored for Bayesian inference, their limited endurance and high programming energy remain critical bottlenecks for on-chip learning.
The objective of this thesis is to investigate ferroelectric field-effect memory transistors (FeMFETs) as building blocks for hardware Bayesian neural networks. The work will involve characterizing and modeling the exploitable ferroelectric randomness for sampling and probabilistic weight updates, designing Bayesian neuron and synapse architectures based on FeMFETs, and evaluating their robustness, energy efficiency, and system-level performance for safety-critical inference under uncertainty.
Adaptive and explainable Video Anomaly Detection
Video Anomaly Detection (VAD) aims to automatically identify unusual events in video that deviate from normal patterns. Existing methods often rely on One-Class or Weakly Supervised learning: the former uses only normal data for training, while the latter leverages video-level labels. Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have improved both the performance and explainability of VAD systems. Despite progress on public benchmarks, challenges remain. Most methods are limited to a single domain, leading to performance drops when applied to new datasets with different anomaly definitions. Additionally, they assume all training data is available upfront, which is unrealistic for real-world deployment where models must adapt to new data over time. Few approaches explore multimodal adaptation using natural language rules to define normal and abnormal events, offering a more intuitive and flexible way to update VAD systems without needing new video samples.
This PhD research aims to develop adaptable Video Anomaly Detection methods capable of handling new domains or anomaly types using few video examples and/or textual rules.
The main lines of research will be the following:
• Cross-Domain Adaptation in VAD: improving robustness against domain gaps through Few-Shot adaptation;
• Continual Learning in VAD: continually enriching the model to deal with new types of anomalies;
• Multimodal Few-Shot Learning: facilitating the model adaptation process through rules in natural language.
Development and Characterization of Terahertz Source Matrices Co-integrated in Silicon and III-V Photonics Technology
The terahertz (THz) range (0.1–10 THz) is increasingly exploited for imaging and spectroscopy (e.g. security scanning, medical diagnostics, non-destructive testing) because many materials are transparent to THz radiation and have unique spectral signatures. However, existing sources struggle to offer both high power and wide tunability: electronic sources (diodes, QCLs) deliver milliwatts but over narrow bands, while photonic emitters (photomixers in III–V semiconductors) are tunable across broad bands but emit only microwatts. This thesis aims to overcome these limitations by developing an integrated matrix of THz sources. The approach is based on photomixing two 1.55 µm lasers in III–V photodiodes to generate a phase-coherent THz current coupled to THz antennas.
Initially, the PhD student will experimentally investigate an existing 16-element THz antenna array (STYX project) CEA-CTReg/DNAQ: setting up the test bench, measuring phase coherence, optical coupling, radiation lobes, and constructive interference. These experiments will provide a scientific foundation for the subsequent design of an integrated photonic array on silicon. The student will simulate the photonic architecture (couplers, waveguides, phase modulators, Si/III–V transitions) synchronizing multiple InGaAs photodiodes. Prototyping will include the fabrication of silicon photonic circuits (CEA-LETI) and THz photodiodes/antennas in InP (III-V Lab or, to be confirmed, Heinrich-Hertz-Institut of the Fraunhofer—HHI), followed by their hybrid integration (bonding, alignment).
This thesis will also rely on close collaboration with the IMS laboratory (Bordeaux), which is nationally and internationally recognized for its expertise in silicon photonics and THz systems. IMS will provide complementary expertise in optical modeling, electromagnetic simulation, and experimental characterization, reinforcing the multidisciplinary strength of the project.
Finally, the ultimate goal of this thesis is to develop a proof-of-concept demonstrator with a few phase-locked THz emitters (e.g. 4–16) will be produced and characterized, showing enhanced beam directivity and output power thanks to constructive interference. This demonstration will pave the way for large-scale THz source arrays with significantly improved range and penetration for advanced THz imaging systems.
AI-Driven Network Management with Large Language Models LLMs
The increasing complexity of heterogeneous networks (satellite, 5G, IoT, TSN) requires an evolution in network management. Intent-Based Networking (IBN), while advanced, still faces challenges in unambiguously translating high-level intentions into technical configurations. This work proposes to overcome this limitation by leveraging Large Language Models (LLMs) as a cognitive interface for complete and reliable automation.
This thesis aims to design and develop an IBN-LLM framework to create the cognitive brain of a closed control loop on the top of an SDN architecture. The work will focus on three major challenges: 1) developing a reliable semantic translator from natural language to network configurations; 2) designing a deterministic Verification Engine (via simulations or digital twins) to prevent LLM "hallucinations"; and 3) integrating real-time analysis capabilities (RAG) for Root Cause Analysis (RCA) and the proactive generation of optimization intents.
We anticipate the design of an IBN-LLM architecture integrated with SDN controllers, along with methodologies for the formal verification of configurations. The core contribution will be the creation of an LLM-based model capable of performing RCA and generating optimization intents in real-time. The validation of the approach will be ensured by a functional prototype (PoC), whose experimental evaluation will allow for the precise measurement of performance in terms of accuracy, latency, and resilience.
Rheology and Conduction of Functional Polymers for Embedded Electronics in 3D/4D Additive Manufacturing
This PhD project, conducted on the MAPP platform (CEA-Metz), focuses on the development of additive manufacturing (3D/4D) processes for the integration of smart materials. The aim is to overcome the limitations of traditional planar electronic architectures (PCBs, wafers) integration by enabling the direct-to-shape printing of electronic functions within 3D parts performed by Fused Deposition Modeling and Paste Extrusion Modeling. The research will address functional conductive polymers, composed of an organic matrix and metallic particles, whose conduction mechanisms (direct contact, tunneling effect, ionic conduction) are governed by the percolation threshold. The study will investigate the processing of these materials, their rheological and electrical behavior, and the exploitation of their resistive, piezoresistive and piezoelectric properties to design novel sensor (3D) and actuator (4D) functions. The doctoral candidate will benefit from advanced characterization facilities and the guidance of a multidisciplinary team with expertise in additive manufacturing, materials science, and microelectronics.