Integrated optical functions on microbolometer focal planes for uncooled infrared imaging
Thermal infrared imaging (wavelengths 8-14 µm) is a growing field, particularly in industry, transportation, and environment. It relies on a detection technology, microbolometers, for which CEA-Leti is at the forefront of the global state of the art. Integrating advanced optical functions directly onto the detectors is a very promising approach for improving performance, compactness, and cost in future infrared cameras.
The optical functions under consideration include spectral filtering, polarimetry, wavefront correction, and more. Some aim to enrich the image with information essential for applications such as absolute thermography (temperature and emissivity measurement), identification for automated scene interpretation (machine vision), gas detection, and others.
The proposed work will include the design, fabrication, and electro-optical characterization of functionalized microbolometer arrays. Using 3D electromagnetic simulation tools, the design of these optical functions will take into account the compatibility with our microbolometer technologies and the capabilities of our microfabrication facilities. Fabrication will take place in the CEA-Leti cleanrooms by dedicated personnel, but the candidate will participate in defining and monitoring the work. Finally, optical and electro-optical characterizations will be performed in our laboratory, if necessary with the development of dedicated characterization benches.
Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment
Power converters are essential in numerous applications such as industry, photovoltaic systems, electric vehicles, and data centers. Their conventional maintenance is often based on fixed schedules, leading to premature replacement of components and significant electronic waste.
This PhD project aims to develop a novel non-invasive and low-cost ultrasound-based monitoring approach to assess the state of health and remaining useful life (RUL) of power converters deployed across various industries.
The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance strategies executed locally (edge deployment).
The research will be carried out within a Marie Sklodowska-Curie Actions (MSCA) Doctoral Network, offering international training, interdisciplinary collaboration, and secondments at leading academic and industrial partners across Europe (Italy and Netherlands for this PhD offer).
Integrated material–process–device co-optimization for the design of high-performance RF transistors on advanced nanometer technologies
This PhD research focuses on the integrated co-optimization of materials, fabrication processes and device architectures to enable high-performance RF transistors on advanced nanometer-scale technologies. The work aims to understand and improve key RF figures of merit—such as transit frequency, maximum oscillation frequency, noise behaviour and linearity—by establishing clear links between material choices, process innovations and transistor design.
The project combines experimental development, structural and electrical characterization, and advanced TCAD simulations to analyse the strengths and limitations of different integration schemes, including FD-SOI and emerging 3D architectures such as GAA and CFET. Particular attention will be given to the engineering of optimized spacers, gate stacks, junction placement and epitaxial source/drain materials in order to minimize parasitic effects and enhance RF efficiency.
By comparing planar and 3D device platforms within a unified modelling and characterization framework, the thesis aims to provide technology guidelines for future generations of energy-efficient RF transistors targeting applications in 5G/6G communications, automotive radar and low-power IoT systems.
Instrumented PCB for predictive maintenance
The manufacturing of electronic equipment, and more specifically Printed Circuit Boards (PCBs), represents a significant share of the environmental impact of digital technologies, which must be minimized. Within a circular economy approach, the development of monitoring and diagnostic tools for assessing the health status of these boards could feed into the product’s digital passport and facilitate their reuse in a second life. In a preventive and prescriptive maintenance perspective, such tools could extend their lifespan by avoiding unnecessary periodic replacement in applications where reliability is a priority, as well as adapting their usage to prevent premature deterioration.
This PhD proposes to explore innovative instrumentation of PCBs using ‘virtual’ sensors, advanced estimators powered by measurement modalities (such as piezoelectric, ultrasonic, etc.) that could be integrated directly within the PCBs. The objective is to develop methods for monitoring the health status of the boards, both mechanically (fatigue, stresses, deformations) and electronically.
A first step will consist of conducting a state-of-the-art review and simulations to select the relevant sensors, define the quantities to be measured, and optimize their placement. Multi-physics modeling and model reduction will then make it possible to link the data to PCB integrity indicators characterizing its health status. The approach will combine numerical modeling, experimental validations, and multiparametric optimization methods.
Electron beam probing of integrated circuits
The security of numerical systems relies on cryptographic chains of trust starting from the hardware up to end-user applications. The root of chain of trust is called a “root of trust” and takes the form a dedicated Integrated Circuit (IC), which stores and manipulates secrets. Thanks to countermeasures, those secrets are kept safe from extraction and tampering from attackers.
Scanning Electron Microscope (SEM) probing is a well-known technique in failure analysis that allows extracting such sensitive information. Indeed, thanks to a phenomenon known as voltage contrast, SEM probing allows reading levels of transistors or metal lines. This technique was widely used in the 90s on ICs frontside, but progressively became impractical with the advance of manufacturing technologies, in particular the increasing number of metal layers. Recent research work (2023) showed that SEM-based probing was possible from the backside of the IC instead of frontside. The experiments were carried-out on a quite old manufacturing technology (135 µm). Therefore, it is now essential to characterize this threat on recent technologies, as it could compromise future root of trusts and the whole chains of trust build on top of them.
The first challenge of this PhD is to build a reliable sample preparation process allowing backside access to active regions while maintaining the device functional. The second challenge is to characterize the voltage contrast phenomenon and instrument the SEM for probing active areas. Once the technique will be mature, we will compare the effect of the manufacturing technology against those threats. The FD-SOI will be specifically analyzed for potential intrinsic benefits against SEM probing.
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.
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.