PhD – student: Machine learning for semiconductor wafer metrology
Work Activities
As electronic chips continue to shrink while becoming increasingly powerful, their fabrication depends on achieving sub-nanometer precision at high speed and efficiency. At the heart of this technological challenge lies the need for accurate and reliable metrology guided by cutting-edge computational methods.
Recent breakthroughs in Machine Learning enable powerful physics-informed models capable of solving the inverse problem of reconstructing metrology parameters directly from low-resolution microscopy images. By embedding this problem in a Bayesian framework, these models not only estimate the desired physical quantities but also provide meaningful uncertainty quantification. Such uncertainty estimates are crucial for both process control in semiconductor manufacturing and broader scientific applications.
In this project, you will develop a novel Machine Learning approach that integrates physical simulations of the measurement process with its inverse reconstruction. The resulting joint model will be capable of simulating measurements from given physical parameters and inferring those parameters from experimental data — all within a Bayesian inference setting.
You will work in close collaboration with the research department at ASML, the Centrum Wiskunde & Informatica (CWI, Prof. dr. Tristan van Leeuwen), and the AI4Science Lab, Informatics Institute, University of Amsterdam (dr. Patrick Forré), combining industrial relevance with academic depth in computational science and mathematical modeling.
Qualifications
You have (or soon will have) a MSc degree in physics, applied mathematics, computer science, or a related discipline, meeting the Dutch university requirements for entry into a PhD program. A background in machine learning, inverse problems, or numerical modeling is desirable. Experience in applying Machine Learning techniques in a scientific context would be a strong plus. You are curious about combining physical modeling with data-driven methods and are motivated to work at the interface of academia and industry. Strong analytical skills, a collaborative mindset, and proficiency in verbal and written English are essential.
Work environment
ARCNL performs fundamental research, focusing on the physics and chemistry involved in current and future key technologies in nanolithography, primarily for the semiconductor industry. While the academic setting and research style are geared towards establishing scientific excellence, the topics in ARCNL’s research program are intimately connected with the interests of the industrial partner ASML. The institute is located at Amsterdam Science Park and currently employs about 100 persons of which 65 are ambitious (young) researchers from all over the globe. www.arcnl.nl
Working conditions
The position is intended as full-time (40 hours / week, 12 months / year) appointment in the service of the Netherlands Foundation of Scientific Research Institutes (NWO-I) for the duration of four years, with a starting salary of gross € 2,968 per month and a range of employment benefits. After successful completion of the PhD research a PhD degree will be granted at a Dutch University. Several courses are offered, specially developed for PhD-students. ARCNL assists any new foreign PhD-student with housing and visa applications and compensates their transport costs and furnishing expenses.
More information?
For further information about the position, please contact Lyuba Amitonova: l.amitonova@arcnl.nl and Maximilian Lipp (m.lipp@arcnl.nl).
Application
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Online screening may be part of the selection.
Diversity code
ARCNL is highly committed to an inclusive and diverse work environment: we want to develop talent and creativity by bringing together people from different backgrounds and cultures. We recruit and select on the basis of competencies and talents. We strongly encourage anyone with the right qualifications to apply for the vacancy, regardless of age, gender, origin, sexual orientation or physical ability.
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