In-situ Monitoring of RF Power Amplifier Circuits Aging for Eco-design and Extended Lifetime
The semiconductor industry, and more specifically the radio-frequency (RF) circuit sector, is facing critical challenges related to eco-design and eco-innovation. These challenges include the need to extend the lifetime of circuits while meeting the growing demands of emerging markets such as 5G and the future 6G. Among these circuits, power amplifiers (PA) play a central role, being both critical components in terms of energy efficiency and key targets for improving robustness against aging and enabling potential reuse.
In this context, in-situ aging monitoring of PAs appears to be a promising approach for developing innovative and sustainable solutions. This research topic is therefore fully aligned with eco-design strategies, leveraging advanced technological platforms such as current and future CMOS SOI technologies, while integrating industrial constraints through existing strategic collaborations with major partners of CEA Leti.
This thesis aims to design an innovative in-situ monitoring solution to evaluate and compensate for the aging of power amplifiers, thereby extending their lifetime through reuse and self-correction strategies. To achieve this, it will rely on methodologies and circuits specifically adapted to practical use cases. The ambition is thus to develop a new generation of robust and durable circuits, integrating intelligent aging management mechanisms. By adopting an eco-design approach, this work aims to address environmental challenges while enhancing the industrial competitiveness of CMOS SOI technologies.
Study of vibration effects on electrical cable diagnosis using reflectometry
This thesis focuses on the effect of vibrations on the diagnosis of electrical cables using reflectometry. Cable systems, which are present in many critical infrastructures such as aeronautics, railways, space systems, and nuclear facilities, are exposed to mechanical and environmental stresses that may lead to soft and intermittent faults. Under vibration, these faults may appear, disappear, or modify their electrical signature, making their detection particularly challenging.
One of the main challenges concerns No Fault Found situations, in which a fault observed during operation becomes non-reproducible once the vibration conditions disappear. Another important issue is the temporary masking of certain faults by vibrations, which may lead to false negatives during diagnosis and delay the detection of latent degradation.
The objective of this thesis is to better understand and model the electromechanical behavior of cable faults subjected to vibrational stress, in order to link vibration profiles, the mechanical and electrical evolution of the fault, and the signatures measured by reflectometry. The work will be based on experiments combining fast reflectometry and a high-speed camera, as well as on the development of models and analysis tools. Experimental and simulated data will then be used to improve the detection, characterization, and prediction of fault evolution, with a view to advanced diagnosis and predictive maintenance.
Enhanced thermal resistor nanomaterials development, based on amorphous-Si, for microbolometers
The aim of this thesis is to develop high-performance materials for the next generation of microbolometers, with a particular focus on increasing the thermal resistance of the supporting arms to enable smaller pixel pitches. Our approach aims to take advantage of the lower thermal conductivity of materials induced by controlled inhomogeneities at the nanoscale. For this purpose, we have already demonstrated the fabrication of nanocrystallized amorphous silicon (nc-aSi) thin films (few tens of nanometers thick) with promising thermal conductivity.
In the case of nc-aSi, a range of characterization techniques—including Raman spectroscopy, X-ray diffraction, transmission electron microscopy (TEM), and the 3? method for thermal conductivity measurements—will be employed to correlate deposition conditions, nanostructure and thermal transport properties, in order to identify strategies for reducing its thermal conductivity.
The knowledge gained from studying nc-aSi could be extended to other materials.
Ideally, combining thermal measurement analysis with theoretical conduction models will provide insight into the mechanisms of heat propagation in these nanocrystallized materials.
Finally, the technological integration potential of these materials within the microbolometer fabrication line will be evaluated, including the mechanical strength and the thermal robustness.
Scalable Network Digital Twins through Adaptive Fidelity Management
Future communication systems such as 6G networks are evolving toward highly distributed, autonomous, and heterogeneous infrastructures integrating cloud-edge continuum architectures, Open RAN (O-RAN), massive IoT deployments, edge computing, and highly dynamic wireless environments.
These systems are expected to support demanding services such as mission-critical communications, industrial automation, autonomous mobility, and immersive applications, operating under highly dynamic traffic conditions, frequent topology changes, fluctuating resource availability, and stringent latency and reliability requirements.
Managing such systems through risk-free configuration, optimization, and evolution operations is becoming increasingly challenging. This is particularly true when performing real-time network optimization, operational what-if analysis, network troubleshooting, or planning network upgrades and extensions.
To address these challenges, recent research initiatives have investigated the application of the Digital Twin paradigm to communication networks, commonly referred to as Network Digital Twins (NDTs).
An NDT is a virtual representation of a communication network that remains sufficiently aligned with the physical infrastructure to reproduce its operational state and behavior, support predictive analysis, and evaluate hypothetical scenarios before applying decisions to the real system.
However, maintaining an accurate and temporally consistent NDT in large-scale and highly dynamic networks remains a major challenge.
Current NDTs predominantly rely on explicit synchronization mechanisms to maintain fidelity between the physical and virtual systems. Although recent works have introduced AI-assisted prediction mechanisms to reduce synchronization overhead, these approaches do not fully address the problem of dynamically adapting the fidelity of the NDT according to predictive uncertainty, information value, network dynamics, and operational requirements. Adaptive fidelity can be interpreted as a multi-resolution representation mechanism, where the NDT dynamically adjusts its observation granularity, synchronization overhead, and reconstruction accuracy according to information value, predictive uncertainty, network dynamics, and available resources. The main objective of this PhD thesis is to design, develop, and validate an Adaptive Fidelity Management framework enabling scalable and resource-efficient Network Digital Twins for future communication systems.
Statistical optimization and calibration of lithography model
This thesis provides an opportunity to develop statistical methods to optimize and calibrate lithography models used to generate optimal photomask designs by mean of optical proximity correction (OPC).
Microelectronic devices with high circuit density are in high demand and are extensively researched and pursued by industries. One way to achieve higher circuit density is to decrease pattern dimension or pitch. However as pattern dimension decreases, fabrication challenge increases. Resolution Enhancement Technique (RET) such as OPC has therefore to be used to generate photomask of such circuits.
OPC aims to improve the wafer pattern fidelity by compensating errors arising due to optical or process effects during fabrication steps. To implement this correction, a lithography model has to be generated taking into account the exposure system and photo resist characteristics. These models are calibrated using very large volume of experimental data which includes CD-SEM measurements and contour extracted from SEM images. The data acquisition and image post processing is a bottleneck in model calibration flow, consuming huge amount of time and resources.
During the period of thesis, work will be focused on:
Innovative test patterns to optimize input data for model calibration
Statistical and algorithmic optimization of model calibration flow
Impact of experimental data variability on lithography models
Device for light extraction through evanescent coupling in Photonic Integrated Circuit
The objective of the PhD is to develop a new class of optical devices used to provide interfaces between Photonics Integrated Circuits (PICs) and free space optics. These devices have been investigated in a seminal work conveyed in a former PhD work. It consists in the use of a nanoimprinted prismatic structure bonded on the surface of a PIC. Through evanescent coupling and reflections in the structure, guided waves can be transferred from the PIC toward an external optical system. With the use of electro-optic materials, this extractor may offer interesting applications as a switchable extractor.
The candidate will delve into the theory of the device to improve its performance. He/she will perform experiments on packaging, holography and PIC characterization. His/her objective will be to manufacture a large panel of sample devices to be tested. One particular concern is to evaluate the behavior of the fabricated devices in a large spectral domain form visible to short Infra-Red wavelengths.
The candidate will use FDTD simulation software to evaluate the propagation characteristics of the wave as it travels from a confined space to a free space. He/she will define optimal prismatic structures to be replicated with nanoimprint. He/she will implement the polymer structure on PIC samples through delicate transfer and bonding protocol in a clean room. He/she will record micro-holographic optical elements with lasers to improve the angular potential of the final device. Large part of the PhD will concern the use of optical set-ups.
Development of a bifunctionnal zwitterionic nano-coating for aptasensors - a new linker for biological probes that hinders non-specific adsorptions
The field of biosensor development frequently encounters the issue of non-specific signals. These signals often limits the performance of biosensors and complicates industrial transfers. The functionalization steps for biosensors design generally include three steps: i) functionalization of the transducer with a linker molecule, ii) immobilization of a biological probe (antibodies, aptamers, oligonucleotides...) using the linker, iii) treatment with an entity to block non-specific interactions. The literature is full of solutions that highlight the blocking of these non-specific interactions with different types of chemical or biological entities: proteins (BSA, casein...), polymers (PEG, PVP) or small molecules (ethanolamine, hexylamine...).
However, an alternative functionalization approach with a linker that offers both the ability to immobilize biological probes while ensuring the blocking of non-specific interactions represents an innovative path for the development of biosensors.
This PhD project aims to explore the design and surface functionalization with a bifunctional nano-coating responding to this approach. Regarding the blocking, zwitterionic polymers will be at the heart of the development. Indeed, numerous studies demonstrate their ability to drastically reduce the interactions of complex biological environments with surfaces that are functionalized with them. Furthermore, it is possible to exploit the chemical functions of certain types of zwitterions to immobilize biological probes on demand. After optimizing their activity in homogeneous phase, aptamers will be immobilized on silicon transducers (QCM-d and photonic chip) via the bifunctional zwitterionic nano-coating. The objective of the thesis is to obtain a proof of concept of a biosensor functionalized with this new linker that ensures the reduction of non-specific signals while ensuring the specific detection of the target considered (Tyrosinamide model) in model and complex environments derived from biomedical sector, such as serum or plasma.
Sharper Structural Insight in Nanoelectronics with Dark-Field X-Ray Microscopy
Dark-field X-ray microscopy (DFXM) is an emerging, non-destructive synchrotron technique capable of imaging strain and crystalline defects with 30–100 nm resolution over large fields of view. Recent upgrades at the ESRF and the ID03 beamline have increased X-ray intensity by two orders of magnitude, enabling investigation of the most challenging nanoscale structures produced in cleanroom environments. This PhD aims to exploit DFXM for the analysis of advanced microelectronic architectures subjected to critical thermo-mechanical stress. DFXM will provide 3D mapping of strain, orientation and buried defects in complex devices without sample destruction. A comparative study will be performed against complementary local X-ray techniques also available at synchrotron facilities such as Laue microdiffraction and scanning X-ray diffraction microscopy. Multi-scale correlations will be established with TEM and Raman spectroscopy. Finite-element simulations will support interpretation by modelling the mechanical behavior under thermal or operational loads. The objective is to define a robust methodology for multiscale strain analysis in microelectronics devices.
This PhD will take place at the CEA–Leti on the Nanocharacterization platform and is embedded in a strong ESRF@ID03 collaboration and supports advances in quantum technologies, photonics and energy-efficient microelectronics. This work will contribute to improved reliability and design optimization of next-generation devices.
Development of ultra-high-resolution magnetic microcalorimeters for isotopic analysis of actinides by X-ray and gamma-ray spectrometry
The PhD project focuses on the development of ultra-high-resolution magnetic microcalorimeters (MMCs) to improve the isotopic analysis of actinides (uranium, plutonium) by X- and gamma-ray spectrometry around 100 keV. This type of analysis, which is essential for the nuclear fuel cycle and non-proliferation efforts, traditionally relies on HPGe detectors, whose limited energy resolution constrains measurement accuracy. To overcome these limitations, the project aims to employ cryogenic MMC detectors operating at temperatures below 100 mK, capable of achieving energy resolutions ten times better than that of HPGe detectors. The MMCs will be microfabricated at CNRS/C2N using superconducting and paramagnetic microstructures, and subsequently tested at LNHB. Once calibrated, they will be used to precisely measure the photon spectra of actinides in order to determine the fundamental atomic and nuclear parameters of the isotopes under study with high accuracy. The resulting data will enhance the nuclear and atomic databases used in deconvolution codes, thereby enabling more reliable and precise isotopic analysis of actinides.
AI model deployment using Hardware-Aware on-chip Fine Tuning
Emerging unconventional hardware technologies are essential for future Edge-AI applications, but they often suffer from variability, mismatches, and technology dispersion. These non-idealities can strongly reduce AI inference accuracy if no fine-tuning or calibration is applied. Traditional supervised fine-tuning is difficult to industrialize because it raises issues related to data confidentiality, service quality, software complexity, and hardware constraints.
This PhD project aims to develop hardware-algorithm co-design methods that avoid the need for fully supervised on-chip retraining. The main goal is to create task-agnostic, inference-level self-calibration strategies able to compensate hardware mismatches at the system level. The work will study existing adaptation methods, including weight-based, feature-based, output-based, and domain adaptation approaches.
The project will define a relevant Edge-AI application, develop a generic fine-tuning method, and validate it through low-level electrical simulations. If possible, the proposed algorithm may also be tested experimentally on a custom ASIC-based hardware setup.