Development of multiplexed photon sources for quantum technologies
Quantum information technologies offers several promises in domains such as computation or secured communications. Because of their robustness against decoherence, photonic qubits are particularly interesting for quantum communications applications, even at room temperature. They also offers an alternative to other qubits technologies for quantum computing. For the large-scale deployment of those applications, it is necessary to have cheap, compact and scalable devices. To reach this goal, silicon photonics platform is attractive. It allows implementing key components such as generation, manipulation and detection of photonic qubits. On the silicon platform, the photonic qubits are generated by pair through non linear process. has several benefits, such as working at room temperature, the ability to generate heralded single photon, or undistiguishable photons with spatially distinct sources.
The goal of this thésis is to work on the development, the fabrication monitoring, and the characterization in the laboratory of multiplexed photon sources on silicon chips to overcome the limits in the process of photon generation with one source. In order to achieve a full integration on chip, it is also essential to properly filter unwanted light in order to keep only the photons that are of interest. As a consequence you will also focus on the development of intgrated filters with high rejection rate.
Injection-Locked Oscillators based Liquid Neural Networks for Generative Edge Intelligence
This PhD aims to design analog liquid neural networks for generative edge intelligence. Current neuromorphic architectures, although more efficient through in-memory computing, remain limited by their extreme parameter density and interconnection complexity, making their hardware implementation costly and difficult to scale. The Liquid Neural Networks (LNN), introduced by MIT at the algorithmic level, represent a breakthrough: continuous-time dynamic neurons capable of adjusting their internal time constants according to the input signal, thereby drastically reducing the number of required parameters.
The goal of this PhD is to translate LNN algorithms into circuit-level implementations, by developing ultra-low power time-mode cells based on oscillators that reproduce liquid dynamics, and interconnecting them into a stable, recurrent architecture to target generative AI tasks. A silicon demonstrator will be designed and validated, paving the way for a new generation of liquid neuromorphic systems for Edge AI.
Analysis and design of dispersion-engineered impedance surfaces
Dispersion engineering (DE) refers to the control of how electromagnetic waves propagate in a structure by shaping the relationship between frequency and phase velocity. Using artificially engineered materials and surfaces, this relationship can be tailored to achieve non-conventional propagation behaviors, enabling precise control of dispersive effects in the system. In antenna design, dispersion engineering can enhance several key aspects of radiation performance, including gain bandwidth, beam-scanning accuracy, and in general the reduction of distortions that arise when the operating frequency changes. It can also enable additional functionalities, such as multiband operation or multifocal behavior in lens- and reflector-based antennas.
This thesis aims to investigate the underlying physics governing the control of phase and group velocities in two-dimensional artificial surfaces with frequency-dependent effective impedance properties. A particular emphasis will be placed on spatially fed architectures, such as transmitarrays and reflectarrays, where dispersion plays a crucial role. The objective is to derive analytical formulations within simultaneously control of both group and phase delay, develop general models, and assess the fundamental limitations of such systems in radiation performance. This work is especially relevant for high-gain antenna architectures, where the state of the art remains limited. Current dispersion-engineered designs are mostly narrowband, and no compact high-gain solution (> 35 dBi) has yet overcome dispersion-induced degradations, which lead to gain drop and beam squint.
The student will develop theoretical and numerical tools, investigate new concepts of periodic unit cells for the impedance surfaces, and design advanced antenna architectures exploiting principles such as true-time delay, shared-aperture multiband operation, or near-field focsuing with minimized chromatic aberrations. The project will also explore alternative fabrication technologies to surpass the constraints of standard PCB processes and unlock new dispersion capabilities.
Optically Pumped Magnetometers based on helium-3
The laboratory, reknown for its expertise in high-resolution and high-precision magnetic measurements, has been developing and providing for several decades successive generations of optically pumped helium-4 magnetometers. These instruments serve as reference sensors aboard the ESA Swarm mission satellites launched in late 2013, and will also equip the forthcoming NanoMagSat mission, scheduled to launch from the end of 2027 onward.
In an effort to diversify its activities and to address emerging applications involving autonomous or “deploy-and-forget” sensors, where power consumption constraints are particularly demanding, the laboratory now aims to develop a new magnetometer technology based on helium-3 atoms as the sensitive medium. The lifetime of the helium-3 atomic state used for magnetic field measurement is significantly longer than that of the equivalent helium-4 state. This property enables a substantial reduction in optical pumping requirements, thereby offering the prospect of improved energy efficiency and power consumption.
The objective of this research is to advance the Technology Readiness Level (TRL) of this helium-3-based magnetometer architecture, with the ultimate goal of realizing an instrument that combines outstanding metrological performance with exceptional energy frugality, suited to these highly specific and constrained applications.
Accordingly, the purpose of this PhD work will be to design, implement, and experimentally evaluate a helium-3 magnetometer architecture capable of fulfilling these performance and efficiency objectives.
Topologic optimization of µLED's optical performance
The performance of micro-LEDs (µLEDs) is crucial for micro-displays, a field of expertise at the LITE laboratory within CEA-LETI. However, simulating these components is complex and computationally expensive due to the incoherent nature of light sources and the involved geometries. This limits the ability to effectively explore multi-parameter design spaces.
This thesis proposes to develop an innovative finite element method to accelerate simulations and enable the use of topological optimization. The goal is to produce non-intuitive designs that maximize performance while respecting industrial constraints.
The work is divided into three phases:
- Develop a fast and reliable simulation method by incorporating appropriate physical approximations for incoherent sources and significantly reducing computation times.
- Design a robust topological optimization framework that includes fabrication constraints to generate immediately realizable designs.
- Realize such a metasurface on an existing shortloop in the laboratory. This part is optional and will be tackled only if we manage to seize an Opportunity to finance the prototype, via the inclusion of the thésis inside the "metasurface
topics" of european or IPCEI projets in the lab .
The expected results include optimized designs for micro-displays with enhanced performance and a methodology that can be applied to other photonic devices and used by other laboratories from DOPT.
Modeling and characterization of CFET transistors for enhanced electrical performance
Complementary Field Effect Transistors (CFETs) represent a new generation of vertically stacked CMOS devices, offering a promising path to continue transistor miniaturization and to meet the requirements of high-performance computing.
The objective of this PhD work is to study and optimize the strain engineering of the transistor channel in order to enhance carrier mobility and improve the overall electrical performance of CFET devices. The work will combine numerical modeling of technological processes using finite element methods with experimental characterization of crystalline deformation through transmission electron microscopy coupled with precession electron diffraction (TEM-PED).
The modeling activity will focus on predicting strain distributions and their impact on electrical properties, while accurately accounting for the complexity of the technological stacks and critical fabrication steps such as epitaxy. In parallel, the experimental work will aim to quantify strain fields using TEM-PED and to compare these results with simulation outputs.
This research will contribute to the development of dedicated modeling tools and advanced characterization methodologies adapted to CFET architectures, with the goal of improving spatial resolution, measurement reproducibility, and the overall understanding of strain mechanisms in next-generation transistors.
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.