Internship - Imaging Physics R&D

Introduction

ASML US brings together the most creative minds in science and technology to develop lithography machines that are key to producing faster, cheaper, more energy-efficient microchips. We design, develop, integrate, market and service these advanced machines, which enable our customers - the world's leading chipmakers - to reduce the size and increase the functionality of their microchips, which in turn leads to smaller, more powerful consumer electronics.

Our headquarters are in Veldhoven, the Netherlands, and we have 18 office locations around the United States including main offices in Chandler Arizona, San Jose and San Diego California, Wilton Connecticut, and Hillsboro Oregon.

The Imaging Physics R&D team is based in San Jose, dedicated to imaging-related multi-physics research topics including photomask, projection optics, photoresist, etc. We strive to provide the best computational lithography solutions to our customers with accurate and fast optimization algorithms on photomask design, scanner configuration, and scanner dynamic control. The Imaging Physics R&D team also works closely with our headquarters in the system and module design of ASML's next generation scanners.

Roles and Responsibilities

As a Imaging Physics R&D Intern, you will support research and development activities focused on advanced photomask modeling and computational lithography technologies. You will help evaluate new concepts, develop simulation capabilities, and analyze complex physical phenomena related to semiconductor manufacturing.

• Conduct research studies related to advanced photomask modeling and computational lithography.
• Develop and apply simulation methods to investigate optical and electromagnetic effects in photomask systems.
• Analyze modeling results and identify opportunities to improve simulation accuracy and efficiency.
• Support the development of numerical algorithms used in imaging and lithography applications.
• Collaborate with researchers and engineers on next-generation scanner and lithography technologies.
• Document technical findings and present results through reports and technical discussions.
• Contribute to exploratory path-finding projects that support future product and research initiatives.

Education and Experience

• Currently pursuing a bachelor's degree, master's degree, or PhD in Physics, Applied Physics, Electrical Engineering, Optical Engineering, Computer Science, or a related technical discipline.
• Experience through coursework, research, academic projects, or internships involving optics, electromagnetics, computational physics, or semiconductor technology.
• Familiarity with scientific modeling, simulation, and numerical analysis techniques.
• Experience programming in Python, C++, or similar technical computing languages.
• Exposure to computational electromagnetics, image processing, optimization methods, or machine learning is preferred.
• Experience working in Linux-based development or research environments is beneficial.

Skills

• Working at the cutting edge of tech, you'll always have new challenges and new problems to solve - and working together is the only way to do that.
• You won't work in a silo.
• Instead, you'll be part of a creative, dynamic work environment where you'll collaborate with supportive colleagues.
• There is always space for creative and unique points of view.
• You'll have the flexibility and trust to choose how best to tackle tasks and solve problems.

• Apply knowledge of optics and semiconductor lithography to solve complex engineering challenges.
• Utilize computational electromagnetics methods and electromagnetic solvers to model physical systems.
• Develop scientific software using Python, C++, or related programming languages.
• Work effectively in Linux-based development and simulation environments.
• Apply numerical methods and simulation techniques to investigate physical phenomena.
• Analyze large datasets and simulation outputs to generate actionable insights.
• Utilize optimization, image processing, computational geometry, or machine learning techniques to improve modeling performance.
• Communicate technical findings clearly through documentation and presentations.
• Collaborate effectively within multidisciplinary research and engineering teams.
• Demonstrate scientific curiosity, critical thinking, and strong problem-solving skills.

• Inclusion and diversity
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