Development and Characterization of Terahertz Source Matrices Co-integrated in Silicon and III-V Photonics Technology
The terahertz (THz) range (0.1–10 THz) is increasingly exploited for imaging and spectroscopy (e.g. security scanning, medical diagnostics, non-destructive testing) because many materials are transparent to THz radiation and have unique spectral signatures. However, existing sources struggle to offer both high power and wide tunability: electronic sources (diodes, QCLs) deliver milliwatts but over narrow bands, while photonic emitters (photomixers in III–V semiconductors) are tunable across broad bands but emit only microwatts. This thesis aims to overcome these limitations by developing an integrated matrix of THz sources. The approach is based on photomixing two 1.55 µm lasers in III–V photodiodes to generate a phase-coherent THz current coupled to THz antennas.
Initially, the PhD student will experimentally investigate an existing 16-element THz antenna array (STYX project) CEA-CTReg/DNAQ: setting up the test bench, measuring phase coherence, optical coupling, radiation lobes, and constructive interference. These experiments will provide a scientific foundation for the subsequent design of an integrated photonic array on silicon. The student will simulate the photonic architecture (couplers, waveguides, phase modulators, Si/III–V transitions) synchronizing multiple InGaAs photodiodes. Prototyping will include the fabrication of silicon photonic circuits (CEA-LETI) and THz photodiodes/antennas in InP (III-V Lab or, to be confirmed, Heinrich-Hertz-Institut of the Fraunhofer—HHI), followed by their hybrid integration (bonding, alignment).
This thesis will also rely on close collaboration with the IMS laboratory (Bordeaux), which is nationally and internationally recognized for its expertise in silicon photonics and THz systems. IMS will provide complementary expertise in optical modeling, electromagnetic simulation, and experimental characterization, reinforcing the multidisciplinary strength of the project.
Finally, the ultimate goal of this thesis is to develop a proof-of-concept demonstrator with a few phase-locked THz emitters (e.g. 4–16) will be produced and characterized, showing enhanced beam directivity and output power thanks to constructive interference. This demonstration will pave the way for large-scale THz source arrays with significantly improved range and penetration for advanced THz imaging systems.
AI-Driven Network Management with Large Language Models LLMs
The increasing complexity of heterogeneous networks (satellite, 5G, IoT, TSN) requires an evolution in network management. Intent-Based Networking (IBN), while advanced, still faces challenges in unambiguously translating high-level intentions into technical configurations. This work proposes to overcome this limitation by leveraging Large Language Models (LLMs) as a cognitive interface for complete and reliable automation.
This thesis aims to design and develop an IBN-LLM framework to create the cognitive brain of a closed control loop on the top of an SDN architecture. The work will focus on three major challenges: 1) developing a reliable semantic translator from natural language to network configurations; 2) designing a deterministic Verification Engine (via simulations or digital twins) to prevent LLM "hallucinations"; and 3) integrating real-time analysis capabilities (RAG) for Root Cause Analysis (RCA) and the proactive generation of optimization intents.
We anticipate the design of an IBN-LLM architecture integrated with SDN controllers, along with methodologies for the formal verification of configurations. The core contribution will be the creation of an LLM-based model capable of performing RCA and generating optimization intents in real-time. The validation of the approach will be ensured by a functional prototype (PoC), whose experimental evaluation will allow for the precise measurement of performance in terms of accuracy, latency, and resilience.
Hybrid Compression of Neural Networks for Embedded AI: Balancing Efficiency and Accuracy
Convolutional Neural Networks (CNNs) have become a cornerstone of computer vision, yet deploying them on embedded devices (robots, IoT systems, mobile hardware) remains challenging due to their large size and energy requirements. Model compression is a key solution to make these networks more efficient without severely impacting accuracy. Existing methods (such as weight quantization, low-rank factorization, and sparsity) show promising results but quickly reach their limits when used independently. This PhD will focus on designing a unified optimization framework that combines these techniques in a synergistic way. The work will involve both theoretical aspects (optimization methods, adaptive rank selection) and experimental validation (on benchmark CNNs like ResNet or MobileNet, and on embedded platforms such as Jetson, Raspberry Pi, and FPGA). An optional extension to transformer architectures will also be considered. The project benefits from complementary supervision: academic expertise in tensor decompositions and an industrial-oriented partner specialized in hardware-aware compression.
Rheology and Conduction of Functional Polymers for Embedded Electronics in 3D/4D Additive Manufacturing
This PhD project, conducted on the MAPP platform (CEA-Metz), focuses on the development of additive manufacturing (3D/4D) processes for the integration of smart materials. The aim is to overcome the limitations of traditional planar electronic architectures (PCBs, wafers) integration by enabling the direct-to-shape printing of electronic functions within 3D parts performed by Fused Deposition Modeling and Paste Extrusion Modeling. The research will address functional conductive polymers, composed of an organic matrix and metallic particles, whose conduction mechanisms (direct contact, tunneling effect, ionic conduction) are governed by the percolation threshold. The study will investigate the processing of these materials, their rheological and electrical behavior, and the exploitation of their resistive, piezoresistive and piezoelectric properties to design novel sensor (3D) and actuator (4D) functions. The doctoral candidate will benefit from advanced characterization facilities and the guidance of a multidisciplinary team with expertise in additive manufacturing, materials science, and microelectronics.