Quantum Cascade III-V/Si laser micro-sources

This thesis project focuses on the development of innovative micro-laser sources by combining III-V Quantum Cascade materials with Silicon Photonic Crystals. By integrating these advanced technologies, we aim to create hybrid lasers emitting in the middle infrared. This approach has significant advantages for medium-infrared spectrometry (MIR), a crucial technique for the chemical detection of gaseous, solid and liquid compounds.
The CEA-LETI Optical Sensor Laboratory offers a state-of-the-art research environment, where the candidate will have the opportunity to design, model, manufacture and characterize these devices. This thesis is part of a competitive but promising context, where technological advances could open new perspectives in areas such as "well-being and the environment". For Master 2 students who are passionate about photonics and emerging technologies, this research offers an opportunity to actively contribute to innovation in a growing field.

Development of integrated superconducting nanowire single photon detectors on silicon for photonic quantum computing

The development of quantum technologies represents a major challenge for the future of our society, in particular to build unhackable communications as well as quantum computers offering computing power well beyond that available with current supercomputers. Photonic quantum bits (or qubits), in the form of single photons, are robust against quantum decoherence and are therefore very attractive for these applications. At CEA-LETI, we are developping an integrated quantum photonics technology on silicon wafers, compatible with industrialization, comprising key building blocks for qubit generation, manipulation and detection on-chip.
The PhD project will be focused on the development of integrated superconducting nanowire single photon detectors, sensitive to the presence of a single photon, required for photonic quantum computing. The objective will be the design of superconducting single photon detectors integrated with ultra-low loss waveguides used for the core of the quantum computing processor, the development of a clean room fabrication process compatible with the existing silicon photonics platform and the characterization of the detector figures of merit (detection efficiency, dark count rate, timing performances) using attenuated lasers. The final goal of the PhD will be the integration of small circuits including several detectors on-chip to characterize the purity and indistinguishability of single photons emitted by a quantum dot source developped in parallel at CEA-IRIG (also located in Grenoble).
This PhD work will be carried out in collaboration between CEA-LETI and CEA-IRIG and will be a strategic cornerstone at the heart of future generations of quantum photonic processors featuring several tens of qubits.

Advancing image sensor security: using deep learning for simultaneous robust and fragile watermarking

This PhD project aims at advancing the field of image sensor security through a comprehensive exploration of recent deep learning techniques applied to both robust and fragile invisible watermarking. In the specific context of embedded image rendering pipelines, this study aims to address the dual challenges of ensuring resistance against intentional attacks to break the mark (robust watermarking) while maintaining a high sensitivity to alterations (fragile watermarking). The goal of this multifaceted design approach is not only to enhance the security of imager data but additionally opens avenues for applications in authentication, tamper and forgery detection, combined with data integrity checking. The research will delve into fields of research from image sensor rendering pipeline design using attention-augmented deep learning models to the intricacies of embedding multiple watermarks simultaneously, addressing the requirement for both robust and fragile characteristics.
This research is therefore an exciting opportunity for PhD candidates showing interest in the intersection of deep learning, image processing, and security. It provides not only a rich academic landscape for impactful scientific contributions but also holds potential for concrete results for upcoming technological transfers. In practice, the work will consists in finding novel algorithmic solutions to improve watermarking performance, designed to deal with most advanced Deep Learning based attacks, while maintaining a high image quality rendering.