"PhD - student: Machine learning for semiconductor wafer metrology"

Job Summary

As electronic chips continue to shrink while becoming increasingly powerful, their fabrication depends on achieving sub-nanometer precision at high speed and efficiency. This project involves developing a novel Machine Learning approach that integrates physical simulations of the measurement process with its inverse reconstruction within a Bayesian inference setting.

Responsibilities

• Develop a Machine Learning approach to solve inverse problems in semiconductor wafer metrology.
• Integrate physical simulations with inverse reconstruction to simulate measurements from given physical parameters and infer parameters from experimental data.
• Work in collaboration with ASML, CWI, and the AI4Science Lab at the University of Amsterdam.

Qualifications and Requirements

• Hold or soon hold a MSc degree in physics, applied mathematics, computer science, or a related discipline, meeting Dutch university requirements for a PhD program.
• Desirable background in machine learning, inverse problems, or numerical modeling.
• Experience in applying Machine Learning techniques in a scientific context is a strong plus.
• Be curious about combining physical modeling with data-driven methods and motivated to work at the interface of academia and industry.
• Possess strong analytical skills, a collaborative mindset, and proficiency in verbal and written English.

What the Employer Offers

The position is a full-time appointment for four years with a starting salary of up to €2,968 gross per month. It includes employment benefits, assistance with housing and visa for foreign PhD students, and the opportunity to obtain a PhD degree from a Dutch university. The work environment is at ARCNL, focusing on fundamental research in nanolithography, located at Amsterdam Science Park.