PhD: Physics-Informed Machine Learning for Semiconductor Metrology

This four-year PhD position at the Advanced Research Center for Nanolithography (ARCNL) focuses on integrating machine learning with physical simulations to improve semiconductor metrology. The research involves recovering nanoscale structural properties from low-resolution and noisy optical measurements while optimizing experimental configurations. The candidate will collaborate closely with industrial partner ASML, Centrum Wiskunde & Informatica (CWI), and the University of Amsterdam's AI4Science Lab. The successful applicant is expected to conduct fundamental research, publish findings, and contribute to cutting-edge computational science at the intersection of academia and industry.

• Develop novel physics-informed machine learning models that combine physical simulations with inverse reconstruction techniques.
• Investigate data-driven design methods to optimize experimental setups and measurement configurations.
• Analyze noisy, low-resolution measurement data to infer hidden physical properties of nanoscale structures.
• Collaborate actively with academic partners at CWI and the University of Amsterdam, as well as industrial researchers at ASML.
• Disseminate research outcomes through peer-reviewed academic publications and presentations.
• Contribute to the academic community of the institute and participate in specialized doctoral training courses.