Research Topic: Predictive Multiscale Modeling of Boron Diffusion and Activation Kinetics in SiGe

About us: The Institute for Microelectronics at TU Wien is a globally recognized research institute in micro- and nanoelectronics. We cover all aspects of analyzing and investigating microelectronic devices experimentally and theoretically. This includes studying the relevant materials down to the atomistic level (DFT, MD), simulating and modeling the fabrication of complex geometries (e.g., process TCAD), and studying the operation and reliability of advanced nano-scale devices (e.g., Monte Carlo simulations and/or device TCAD).

Description: Technology computer-aided design (TCAD) is a key enabler for next-generation CMOS technologies (FinFETs, nanosheets, gate-all-around), where tight thermal budgets require predictive, physics-based process models. Boron is the key p-type dopant in silicon and SiGe, yet its diffusion and activation kinetics are still poorly understood. As a result, industrial TCAD often relies on empirical, technology-specific parameterizations with limited transferability and physical insight.

This project aims to replace these empirical descriptions with a predictive multiscale toolchain. It combines first-principles (DFT) calculations with machine-learned interatomic potentials, large-scale molecular dynamics, and kinetic Monte Carlo to identify diffusion mechanisms and extract macroscopic parameters (e.g., diffusion
coefficients and reaction rates). These are then formulated as continuum reaction–diffusion models for integration into an industrial TCAD workflow.

The PhD project is fully funded for three years (with possible extension) and offers a highly industry-relevant topic with a direct path to impact in semiconductor technology development.

Your profile

For the available PhD position, we expect the candidate to fulfill the following requirements:

• Master degree in Physics, Chemistry, Materials Science, or a related field
• Strong background in physics (solid-state, quantum mechanics)
• Programming skills (Python) and routine work on GNU/Linux
• Team player, eager, and reliable
• Proactive and self-reliant
• Very good written and spoken English

Additional expertise in the following fields is a plus but is not strictly required:

• Prior experience with DFT (e.g., CP2K, VASP, Quantum ESPRESSO), MD (e.g., LAMMPS), KMC, or related multiscale methods
• Experience with machine learning workflows and/or high-performance computing 

Starting Date as soon as possible

Salary The position is subject to the collective agreement of the TU Wien for scientific workers and employees, employment group B1. The monthly salary is paid 14 times per year, and the annual gross salary is approximately 52.865 EUR.

Application Please submit your application containing a detailed CV, your collective academic certificates, your Master’s thesis (weblink or PDF), and a single-page motivation letter (discussing relevant previous experience related to the topic and desired skills) to jobs@iue.tuwien.ac.at.

Application Deadline There is no particular deadline for applying. The position remains open until filled.