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 durable and flexible KNN piezoelectric materials: toward an alternative to lead-based ceramics and fluorinated polymers
The project aims to develop lead-free and PFAS-free (perfluoroalkyl and polyfluoroalkyl substances) piezoelectric thin films based on potassium sodium niobate (KNN) that are compatible with flexible substrates, in direct response to the growing regulatory and environmental constraints affecting conventional piezoelectric materials. PZT ceramics (lead titanate-zirconate) and PVDF polymers (polyvinylidene fluoride), which currently dominate the market, have significant limitations related to lead toxicity and the environmental persistence of PFAS, respectively. In this context, identifying sustainable and integrable alternative materials is a strategic priority for the CEA, particularly for flexible electronics applied to medical, embedded, and sustainable devices.
KNNs are among the most promising alternatives due to their high piezoelectric properties and high Curie temperature. However, their integration in the form of thin films remains severely limited by crystallization temperatures exceeding 600 °C, which are incompatible with polymer substrates. The project’s objective is to overcome this barrier by developing an innovative sol-gel combustion deposition process, enabling localized or global crystallization at low temperatures (<350 °C), compatible with flexible substrates. Beyond the KNN system, this approach could constitute
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.
Characterisation of the physico-chemical properties of solid residues from biomass hydrothermal carbonisation
Hydrothermal carbonisation (HTC) is a thermochemical conversion process performed in pressurized water (2-6 MPa) between 180 and 260°C. The main product is a carbonaceous solid residue (hydrochar). Various applications are foreseen for hydrochar: combustion, gasification, adsorption, catalysis, soils amendment, hard carbon for Na-ion batteries, …, each of them requiring specific properties.
The objective of the thesis is to characterise and better understand the origin of several physico-chemical properties of biomass hydrochars. A special attention will be paid to hydrophobicity and drying capacity, to physical and textural characteristics of the particles (porosity, granulometry, specific surface), as well as to chemical characteristics (composition). The influence of biomass type and HTC conditions on these properties will be investigated.
The approach will consist in: experimentations in batch reactors on pre-selected biomass resources, together with use of different characterisation techniques for hydrochars; analysis of results aiming at determining links between the characteristics, elucidating the links between the resource and its hydrochar properties as a function of operational conditions.
Dual Active Bridge Topology Based on SiC Synthetic Switches for Ultra-Fast Active Stabilization of a Low-Inertia Converter-Dominated DC Grid.
With the massive deployment of direct current (DC) technologies on the grid, particularly photovoltaics and grid-connected battery energy storage systems (BESS), a growing share of electrical energy now flows through static power converters. Unlike classical grids dominated by rotating machines, which benefit from high natural inertia, power-electronics-dominated networks exhibit very limited inertia and may therefore experience highly dynamic voltage spikes, voltage drops, or even complete collapse. Some research focuses on synthetic inertia, emulated through specific control strategies implemented in static converters, but these approaches depend on equipment manufacturers and do not rely on established standardization. Another approach consists in designing dedicated equipment specifically intended for the active stabilization of low-inertia power systems, which is the direction explored in this PhD project.
A particularly demanding case concerns MVDC grids, which by construction rely entirely on static power converters, therefore exhibiting extremely low natural inertia, and requiring the use of converters based on specific technologies. Within the framework of this PhD, we propose the study and proof of concept of a converter connected to an MVDC electrical network operating between 6 and 12 kV, capable of injecting or absorbing very high levels of power in a transient manner, on the order of ten megawatts for durations ranging from 10 µs to 100 ms. The system will rely on an isolated Dual Active Bridge (DAB) topology, with a medium voltage capacitive DC bus at its primary.
This power electronics topic presents several technological bottlenecks. Synthetic switches (series-connected SiC devices, as investigated in a previous PhD in the laboratory) will have to be implemented in a real DAB converter. A highly isolated power supply for the gate drivers of these synthetic switches will need to be designed. The medium-frequency DAB transformer must be designed to transfer very high transient power while minimizing volume. Particular attention will therefore be paid to transient-oriented design, with the objective of identifying the key parameters that maximize, within a complex structure, the ratio between the converter rated power and its peak power.
Potential extensions toward other pulsed-power applications that could benefit from such a converter will be explored, taking into account their specific constraints.
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.
How defects nucleation affects the the fracture on the SmartCut process
The SmartCut™ technology is widely used in microelectronics for the fabrication of innovative substrates, such as SOI (Silicon-on-Insulator).
The physical phenomena underlying SmartCut™ technology remain one of principal interest of our research. Optimizing the fracture stage is a major focus in our laboratory and in our collaboration with Soitec. Salomon's PhD thesis (expected completion December 2026), the development of post-fracture surface analysis protocols highlighted the link between the evolution of cristalline defects that cause fracture (platelets) and post-fracture surface roughness. We were thus able to characterize the early stages of platelet growth and determine their main characteristics (size and density). This had previously only been achieved through complex characterizations based on TEM observations.
Now that we have highlighted the impact of platelets on post-fracture surface roughness, the next step is to investigate and identify ways to control their nucleation using new processes. This will also involve optimizing the post-fracture state of SOI substrates.
CdTe for medical radiography; control of electrical properties
The use of direct-conversion detectors in medical radiography opens up new possibilities. Due to its properties, the semiconductor material CdTe has emerged as the material of choice for manufacturing these new components. The proposed thesis topic aims to develop the knowledge and processes necessary to produce CdTe crystals with properties tailored to specific application requirements. The work will draw on the laboratory’s advanced expertise in mastering CdTe single-crystal growth processes. The key challenges of the project will be as follows:
- Performing annealing under controlled atmospheres (ex-situ, on small samples) to study their impact on the electrical properties of CdTe,
- Conducting advanced characterizations to better understand the doping mechanisms in CdTe,
- Fabricating “simple” devices and testing them under X-ray flux to quantify the performance of the laboratory’s materials.
The proposed thesis topic is central to the development of a CdTe technology for medical radiography applications. Multidisciplinary work (material and process development, material characterization, fabrication and X-ray testing of simplified devices) is proposed to address this topic.
Energy-minimizing associative neural networks using resistive memories
This PhD project aims to develop Hopfield-type associative neural networks that perform inference through energy-minimizing dynamics.
The goal is to exploit these dynamics for image denoising and reconstruction close to the sensor, under strict energy and latency constraints.
The network synapses will be implemented in ReRAM crossbar arrays, enabling analog in-memory matrix-vector operations.
The work will focus on architecture dimensioning while accounting for array size, weight quantization, device variability and endurance limits.
Reference models will be developed in PyTorch to evaluate alternative neural dynamics and hardware mapping strategies.
Patch-wise image denoising will serve as the main use case to quantify trade-offs between reconstruction quality, latency and energy consumption.
Particular attention will be paid to the robustness of the networks against hardware non-idealities such as noise, variability and memory drift.
The project will also investigate local on-chip learning mechanisms, allowing slow adaptation to changes in the sensor, scene or memory devices.
These learning rules must remain compatible with the endurance constraints of resistive memories.
Ultimately, the PhD should provide hardware-sizing guidelines and support the design of an experimental test vehicle.
The broader scientific objective is to demonstrate that dynamic associative inference can become an efficient, robust and low-power building block for edge AI.