Post-Doc (M/F) in Microelectronics: Atomistic Simulation for Neuromorphic Devices
We are looking for a Post-Doc candidate to conduct research in the European H2020 NEURONN project in collaboration with several academic and industrial partners.
The objective of this work is on device modeling and simulation of VO2 and MoS2 memristors. The goal is to explore device simulation approaches starting from ab initio (first-principle) methods, going through TCAD device modeling and up to SPICE circuit simulations and modeling. The main motivation for using such a multi-scaled approach is that the complexity simulation is reduced while preserving device physics’ accuracy. The first objective is to develop detailed modeling with an accurate description of the electronic structure, surface defects and atomistic growth requires computations based on the quantum-mechanical (QM) methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD). The second objective is to develop an accurate and realistic description of device geometry and characteristics, such as doping, thermal and electron transport, demands continuous modeling approximations applied to systems with millions of atoms and realistic device architectures, e.g. Drift-Diffusion (DD) and Non-Equilibrium Green’s Function (NEGF) formalism. Therefore, the goal is to develop a multi-scale simulation based on different modeling approaches, so it represents the device physics (of VO2 and MoS2) as accurately as possible.
Excellent and self-motivated candidates with a PhD degree in Electrical Engineering, Computer Engineering, Applied Physics, Engineering Physics, Solid-state Physics, Computational Physics or Materials Science with very good marks.
Experience in first-principles atomistic simulations (time-dependent density-functional theory calculations, molecular dynamics, etc.)
Good scripting and programming skills
Excellent written and oral communication skills in English
Readiness to work in an international team and closely collaborate with experimentalists
PhD (M/F) in Microelectronics: Architecture Design for Oscillatory Neural Networks
We are looking for a PhD student that will investigate neuro-inspired computing architecture where information is encoded in the phase of coupled oscillating neurons or oscillatory neural networks (ONN).
The objective of this work is to investigate the full potential of ONN circuits and architectures. In particular, understanding of the interplay between MIT devices and coupling strengths via 2D memristors on phase synchronization, phase difference and scalability to build large-scale ONN architectures. We will also investigate MIT device and 2D memristor process variations and impact on ONN architecture performance and power efficiency. Ultimately, we will investigate and assess the application of associate learning problems such as pattern recognition on ONN architecture.