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Internship in Physics and Mathematics: Data Analysis for Vacuum Systems
2 maanden geleden
Internship Overview
Are you currently pursuing a bachelor's or master's degree in Applied Physics, Mathematics, or Data Science and seeking an internship opportunity? This position may align perfectly with your academic goals.
Department Background
The Machine Conditioning Defectivity & OLT department is dedicated to creating and implementing strategies that safeguard the optical column, reticles, and wafers within the scanner. The solutions we engineer are integral to the ASML portfolio, ensuring the longevity of EUV optics (OLT) by preventing particle contamination on production wafers and reticles.
The vacuum architecture group operates within the Machine Conditioning Defectivity & OLT Solutions department. This team possesses the expertise to specify, model, design, and evaluate gas flow systems and their performance under sub-atmospheric conditions in the EUV scanner.
Internship Responsibilities
The vacuum infrastructure of EUV scanners comprises a sophisticated system of interrelated modules. These modules provide the necessary pressure and flow conditions essential for the operation of EUV machines.
A network of sensors monitors vacuum conditions and hardware to maintain specified operational ranges. Analyzing sensor data collected from a wide array of machines can facilitate the implementation of statistical process control methods. These methods can enhance product development by minimizing machine-to-machine variability, identifying new failure modes, and recognizing production trends.
The objectives of this internship include:
- Developing software tools to integrate statistical process control for vacuum systems data and signals.
- The initial phase will concentrate on analyzing fleet data, testing, and designing statistical rules based on data distributions and temporal behavior. Collaborating with relevant hardware owners, outliers and trends will be associated with potential failure modes and physical processes.
- The subsequent phase will involve creating dashboards and automating statistical process evaluations for real-time SPC monitoring (single- and/or multi-variate).
Candidate Profile
To be an ideal candidate for this position, you should possess the following qualifications:
- Currently a bachelor’s student seeking a graduation internship or a master’s student pursuing an apprenticeship;
- Engaged in technical studies in Applied Mathematics, Data Science, (Applied) Physics, or a related field;
- Competent in Python scripting, MATLAB programming, and statistical analysis;
- A collaborative team player with a creative mindset, proactive attitude, and strong analytical capabilities;
- Fluent in English, both spoken and written.
Additional Requirements
Candidates must also meet the following criteria:
- This internship is for a minimum duration of 6 months.
- Full-time commitment of 40 hours per week (5 days).
- Must be enrolled at a higher education institution for the entire internship duration.
- Must be located in the Netherlands for the internship.
Company Information
ASML is a high-tech enterprise based in the Netherlands, specializing in the manufacture of intricate lithography machines utilized by chipmakers to produce integrated circuits, or computer chips. Our innovations are fundamental to the electronic devices that keep us connected and informed.
Our workforce comprises some of the most inventive minds in physics, electrical engineering, mathematics, chemistry, mechatronics, optics, mechanical engineering, and computer science. We believe in continuous improvement and that innovative ideas can emerge from anyone.
Commitment to Diversity
ASML is an Equal Opportunity Employer that values a diverse and inclusive workforce. We are committed to recruiting, hiring, training, and promoting individuals 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 are vital to our success.
