ASML
Internship: Optimize defect image classification using unsupervised machine learning
Introduction
This is an apprentice or graduation internship for a master's student in the field of applied mathematics, computer science or physics who has affinity with programming and preferably has had some courses on machine learning and would like to explore the possibilities of using unsupervised machine learning.
Job Mission
This internship is positioned within the group responsible for qualifying the defect performance of ASML machines. Part of this qualification process involves exposing wafers and inspecting them with a scanning electron microscope (SEM). This creates large quantities of SEM images that need to be classified into different defect and non-defect classes (mostly by hand). Making this process more efficient by automation and better classification schemes is essential for faster and more robust system qualifications.
You will be part of our automatic defect classification (ADC) team and will explore the possibilities of using unsupervised machine learning to make a better classification scheme and improve the ADC accuracy. The assignment consists of the following topics:
- Become familiar with the current classification scheme and evaluate ADC feature maps to identify issues
- Study the different options of unsupervised learning available and determine the most viable candidate for our use-case
- Apply unsupervised learning using our extensive image database and evaluate the effect on ADC accuracy
You are a master student in the field of applied mathematics, computer science/informatics or physics. You have affinity with programming and preferably had some courses on machine learning. You are well versed in either Matlab or Python. You are naturally curious, self-motivated and good at working independently. You are comfortable with presenting progress on your work on a weekly basis and participate actively in team meeting discussions. Furthermore, your communication skills in English are excellent.
This is an apprentice or graduation internship for 4-5 days a week with duration of a minimum 4 months.
Please keep in mind that we can only consider students (who are enrolled at a school during the whole internship period) for our internships and graduation assignments.