Data Science Internship: Data-driven modelling and identification of wafer tables

Data Science Internship: Data-driven modelling and identification of wafer tables

Introduction

The Tables & Clamps Analysis group within ASML’s Development & Engineering (D&E) department is offering an exciting internship opportunity. As an intern, you’ll be part of a dynamic, multidisciplinary team focused on developing, analyzing, qualifying, troubleshooting and improving Wafer Tables/Clamps (WT/C).

In lithography machines, silicon wafers are kept in place on the WT/C during exposure. The surface condition of the WT/C directly impacts the clamped wafer distortion levels therefore affecting the printed pattern quality and customer satisfaction. Data is collected from factory and customer machines to characterize the performance of the wafer tables. Analysis based on that data impacts internal machine optimization, maintenance actions, future designs, and overall market trends.

Your assignment

Several models of WT/C have been installed in customer machines over time, with different design and surface properties. Customers then apply different processes to their wafers, causing different types of interactions between the WT/C and the wafers.

The assignment is to use the data gathered from the machines in the field to relate the performance of the WT/C to the original design properties. We expect a data-driven model which can identify the original WT/C from their field data. This will be used to identify unregistered or unknown WT/C installed in the machines, and if possible to predict their performance given factory data.

This is a master apprentice internship for 6 months, with the possibility for extension. The start date of this internship is as soon as possible or September.

Your profile

To be a perfect fit for this assignment, you:

• Are pursuing a degree in Data Science, Statistics, Mathematics, Computer Science/Engineering, Mechanical Engineering (with a strong interest in data analysis), or a related field;
• Have excellent MATLAB or Python knowledge as an implementation software;
• Preferably have prior knowledge of machine learning and statistical model validation;
• Have strong analytical and problem-solving skills;
• Are proactive and comfortable in reaching out to experts, arranging support independently and researching literature where needed;
• Have good communication skills and willingness to work independently.

Other requirements you need to meet:

• You are enrolled at an educational institute for the entire duration of the internship;
• You need to be located in the Netherlands to perform your internship. If you are currently living/studying outside the Netherlands, your CV/motivation letter must include your willingness to relocate;
• If you are a non-EU citizen studying in the Netherlands, your university must be willing to sign the necessary internship documents (e.g., Nuffic agreement).

Diversity and inclusion

ASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that diversity and inclusion is a driving force in the success of our company.

About Us

ASML is a leading supplier of lithography equipment, used by the world’s top chipmakers to print microchips that are increasingly powerful, fast and energy efficient. Working at ASML is inspiring, no matter what field you're in. That’s because we push the boundaries of technology: if it’s moving the world forward, chances are, we’re behind it. In fact, we’re probably a part of the electronic device you’re using right now.

We’re a global team of more than 32,000 people from 122 different nationalities and counting. Headquartered in Europe’s top tech hub, the Brainport Eindhoven region in the Netherlands, our operations are spread across Europe, Asia and the US.