Artificial Intelligence (AI) increasingly powers safety-critical systems that demand robust, energy-efficient computation, often in environments marked by data scarcity and uncertainty. However, conventional AI approaches struggle to quantify confidence in their predictions, making them prone to unreliable or unsafe decisions.

This thesis contributes to the emerging field of Bayesian electronics, which exploits the intrinsic randomness of novel nanodevices to perform on-device Bayesian computation. By directly encoding probability distributions at the hardware level, these devices naturally enable uncertainty estimation while reducing computational overhead compared to traditional deterministic architectures.

Previous studies have demonstrated the promise of memristors for Bayesian inference. However, their limited endurance and high programming energy pose significant obstacles for on-chip learning applications.

This thesis proposes the use of ferroelectric memory field-effect transistors (FeMFETs)—which offer nondestructive readout and high endurance—as a promising alternative for implementing Bayesian neural networks.

This thrilling PhD position invites you to dive into the groundbreaking field of 2T0C (Two-Transistor, Zero-Capacitor) BEOL FET (Back-End-of-Line Field-Effect Transistor) based neurons and synapses, a revolutionary approach poised to transform neuromorphic computing. As a PhD student, you will be at the forefront of research that bridges advanced semiconductor technology with brain-inspired architectures, exploring how these innovative neuron circuits can emulate synaptic functions and enhance data processing efficiency.
Throughout this project, you will engage in hands-on design and characterization of cutting-edge 2T0C neuron circuits, utilizing state-of-the-art tools and techniques. You’ll collaborate with a dynamic, multidisciplinary team of engineers and researchers, tackling exciting challenges related to device performance and energy optimization.
Your work will involve extensive characterization of BEOL FET devices and circuits. You will have the opportunity to propose, specify and design new memory read architectures, that enables the exploration of multi-level synaptic behaviors toward the implementation of more energy and area efficient next-generation neuromorphic systems.
Join us for this unique opportunity to push the boundaries of technology and be part of a transformative journey that could redefine the future of computing! Your contributions could pave the way for breakthroughs in brain-inspired systems, making a lasting impact on the field.

As electronic architectures become increasingly complex and dense, managing thermal dissipation is a critical challenge to ensure system reliability and performance. In constrained environments and demanding applications, localized hotspots require innovative cooling solutions compatible with advanced packaging integrations such as 2.5D and 3D. This PhD project is part of this dynamic and aims to explore wafer-level thermal management approaches, relying in particular on advanced 3D integration processes such as direct bonding.

The PhD candidate will contribute to the design and fabrication of test vehicles incorporating temperature sensors and active thermal structures. The main objective will be to assess the efficiency of novel cooling architectures, with a particular focus on integrating microfluidic channels within the stacks, combined with the use of high thermal conductivity materials. The work will include aspects of thermal (and possibly thermo-mechanical) modeling, cleanroom process development, and experimental characterization.

This research topic, at the crossroads of microelectronics and thermal management, offers a stimulating and interdisciplinary framework, closely aligned with emerging industrial needs in advanced packaging.