Development of mid-infrared sensors and Fourier-transform spectrometers in silicon integrated photonics
Fourier-transform infrared (FTIR) spectrometers have become a reference tool for chemical analysis. One of the next challenges is to integrate them on photonic integrated circuits (PICs), using silicon platforms optimized for the mid-infrared (MIR), in order to improve compactness and scalability. You will simulate key MIR PIC components, with a focus on photonic and thermo-optic modeling, design photolithography masks, follow up on fabrication, and characterize the FTIR spectrometers and sensors. Sensing applications will primarily target the detection and identification of greenhouse gases, but may also extend to plastic pollution and biosensing. Although dedicated engineers will be in charge of silicon wafer fabrication in our cleanrooms, you will be involved in defining specifications and in monitoring in-line quality control to ensure that the fabricated devices perform as intended. You will be responsible for characterizing the FTIR and sensor devices at wafer and chip level on custom mid-infrared optoelectronic test benches. A first generation of non-optimized devices will be available upon your arrival. You will be part of the team upgrading the characterization tools to enable faster, more accurate automated measurements. You will analyze and interpret experimental data to deepen our understanding of the advantages and limitations of integrated FTIR spectrometers compared with their bulkier free-space counterparts. You will be expected to publish research papers and present at international conferences.
Integrated superconducting nanowire single photon detecors on SiN waveguides with photon number resolution capability for photonic quantum computing
Among the various qubit technologies, photonic qubits represent a highly promising approach to the development of quantum computers capable of solving problems beyond the reach of the most powerful supercomputers. The core of photonic quantum computing processors relies on the generation, manipulation, and detection of single photons. In this context, this PhD project at CEA-LETI aims to develop a new generation of high-performance single-photon detectors, offering not only high detection efficiency and low noise, but also photon-number-resolving capabilities, i.e., the ability to discriminate between 0, 1, 2, or more incident photons. This functionality is essential for implementing quantum error correction codes and ultimately enabling the transition from physical qubits to logical qubits. The research will focus on the design of innovative detector circuit architectures, the fabrication of integrated prototypes in CEA-LETI's 200 mm cleanroom, and their experimental characterization at cryogenic temperatures. This work will contribute to the development of a strategic technology for next-generation photonic quantum computers.
Acoustic Interrogation of Passive Sensors Through Metal Walls
This PhD topic addresses critical challenges in the nuclear sector, such as monitoring reactors, fuels, and waste, by eliminating risks associated with drilling metal walls (leaks, contamination) while ensuring resistance to extreme environments (radiation, high temperature, pressure). It also offers applications in the medical field, particularly for implantable devices and non-invasive diagnostics. The primary objective is to develop passive acoustic telemetry systems capable of measuring physical parameters behind a metal wall without requiring electronics on the inaccessible side. Inspired by radio frequency (RF) techniques, this approach aims to overcome the limitations of current sensors, which often cannot penetrate metal barriers or require embedded electronics. The work will build on the expertise of the Systems Department in acoustic modeling, piezoelectric materials, and frequency transposition techniques, while fostering cross-disciplinary collaboration across various CEA entities, particularly in RF and MEMS domains. The expected results include novel passive acoustic telemetry techniques and associated design methods, providing a robust and innovative alternative to existing sensors. Structured in three phases (literature review, development, and experimentation), this thesis is part of a collaborative effort within the CEA, with concrete application potential in nuclear, medical, and industrial fields.
Evaluation of new low-damage GaN etching strategies for the realization of high performance MicroLED
For optical communication and display applications (display, AR/VR), the need for micro-displays with high resolution µLEDs GaN is becoming increasingly important. The formation of the pixel constitutes the critical brick because it generates defects on the sides of the µLED, which limit the emission performance of the device. These degradations have even more impacts on electro-optical performance for aggressive LED pitches.
The subject proposes a comprehensive approach, ranging from the development of new GaN etching strategies limiting damage to the pixel edges, to its characterization through dedicated direct means, and validated on a complete device by an electro-optical test vehicle.
Backpropagation-Free Langevin Sampling for Bayesian Learning on ReRAM Architectures
Bayesian neural networks allow artificial intelligence systems to account for uncertainty in their predictions, but training them requires substantial computational resources. Resistive memories (ReRAM), which can both store data and perform computations directly within electronic circuits, offer a promising solution to reduce this cost.
This PhD project aims to develop a new Bayesian learning method on ReRAM hardware, based on Langevin sampling. This approach efficiently explores neural network parameters by combining random perturbations with gradient estimates. The objective is to exploit the intrinsic physical variability of ReRAM devices to estimate these gradients without relying on conventional backpropagation. The possibility of reusing memory programming attempts will also be investigated to reduce energy consumption and device wear.
The work will combine algorithm development, simulations, and experiments on existing ReRAM circuits at CEA-Leti. Expected outcomes include a demonstration of Bayesian learning on real hardware and an assessment of its performance, accuracy, and energy efficiency compared with existing approaches.
Cryogenic exploration of 1/f and Random Telegraph noise effects in advanced transistors used in Quantum System control/readout Electronics
With the CMOS transistor downscaling, low-frequency noise (LFN) or “1/f noise” due to charge trapping/ detrapping in gate dielectric interface defects has become increasingly important, as its amplitude is inversely proportional to the gate area, thus limiting the performance of mainly analog and mixed signal circuits. For nano-scale transistors, another type of fluctuation appears, called "Random Telegraph Noise" (RTN), due to the activity of only a handful of traps. RTN consists of sudden, high-amplitude transitions of the current, which can jeopardize the stability of memory cells and the precision of imager circuits. Counterintuitively, not only both LFN and RTN present at deep cryogenic conditions, but in certain cases even more prevalent than at room temperature[4]. This is an enormous obstacle for quantum computers, because cryo-CMOS circuits need to be physically very close to the qubit devices in order to perform the tasks of control, readout, and signal amplification. While technological advancements could potentially improve function below 10 K and accurate IC design could mitigate the read/write or readout amplification issues, a significant effort on the physical understanding of the trapping/detrapping cryogenic phenomena and a precise mathematical description is required, not only for noise but also for aging effects such as Bias Temperature Instabilities (BTI).
This thesis aims to provide a deep understanding of trap-related noise effects at cryogenic temperatures down to 4 K through thorough experimental studies, while also developing physics-based compact noise models for cryo-circuit design. Recent interpretation approaches will be evaluated, and new methods of noise parameter extraction will be elaborated. In terms of technologies, studied devices will be FD-SOI MOSFETs 28nm node from ST and used for Q-LOOP project (IRT - UE project.
Development and characterization of bio-based photoresists for lithography applications
Context :
CEA-Leti designs the microelectronic components of tomorrow. This involves developing new materials for devices, as well as essential materials for integrated circuit manufacturing, such as photoresists. These innovations also aim to reduce environmental impact by creating products without PFAS or toxic substances. In this context, the Advanced Lithography Processes Laboratory (LALP) collaborates with the Materials for Sensors and Delivery Laboratory (LMCD) at CEA to co-develop new bio-based lithography resists containing methacrylate functions. The goal of the PhD is to study the performances of these new photoresists in lithography and to evaluated their environmental and economical impact. To do so, an emphasis will be made on the influence of the chemical synthesis on the optical properties of the resists, their polymerization mechanisms, and the mechanical characteristics of the resists after exposure. Finally, tools will be given to conduct life cycle analysis on the different products.
Required skills :
The study will examine the entire photolithography process: from the synthesis of the base product and its formulation, to the final structuring. An initial study will analyze the rheological properties of the material after synthesis. Next, its behavior will be evaluated during the spin-coating step (ellipsometry and TGA/DSC). An analysis will focus on the product's sensitivity to a wavelength of 365 nm. Characterization of the product will be carried out by examining the shape of the obtained patterns (SEM) and the physical properties of the resist (ellipso-porosimetry and AFM). Etching tests will also be conducted.