AI Internship: Intelligent Reticle Health Monitoring Using Reduced Measurement Data

StudentJob; Veldhoven; On-site; EUR 6,700 - 11,000; Full time

Job summary

ASML in the Netherlands invites per-master students to work on AI-driven reticle health monitoring. You will explore ML techniques using limited measurement data and historical datasets, aiming to provide reliable health assessments with fewer measurements.

The internship blends ML, data analysis, and semiconductor engineering in a highly innovative environment. You will collaborate with engineers and data specialists, develop training and testing pipelines, and present actionable insights for

Qualifications

• Must be enrolled in a master’s program and able to commit to a minimum 6 months.
• Experience with machine learning, AI, and model development.
• Programming in Python; MATLAB experience is an advantage.

Responsibilities

• Analyze historical reticle measurement datasets and identify indicators of health changes.
• Create training, validation, and independent test datasets for ML development.
• Develop and train AI/ML models using reduced measurement datasets.
• Compare AI model performance with established engineering analysis methods.
• Investigate the minimum data required for reliable reticle health monitoring.
• Evaluate model robustness, uncertainty, and generalization to unseen data.
• Present findings and recommend efficient AI approaches for reticle health monitoring.

Education

Master's degree in AI/ML/Data Science

Related engineering field

HBO

Tools

Python

MATLAB

TensorFlow

PyTorch

Job description

Introduction

At ASML in Veldhoven, you will join a team working on advanced semiconductor manufacturing technologies. Reticles are critical components in the lithography process, and their condition can influence the accuracy of semiconductor production. Monitoring reticle health currently requires extensive measurement efforts and valuable machine time. During this internship, you will investigate how artificial intelligence can extract meaningful insights from limited measurement datasets while maintaining reliable detection of reticle health changes. This internship offers a unique opportunity to combine machine learning, data science, and engineering in a highly innovative industrial environment.

Your assignment

During this internship, you will develop and evaluate machine learning approaches that help monitor reticle health more efficiently. You will work with historical measurement data and investigate how AI can provide reliable insights using significantly fewer measurements than traditional approaches. You will also explore how existing operational data can contribute to reticle health assessment.

Your main responsibilities will be:

• Analyze historical reticle measurement datasets and identify indicators of reticle health changes.
• Create training, validation, and independent test datasets for machine learning development.
• Develop and train AI and machine learning models using reduced measurement datasets.
• Compare AI model performance with established engineering analysis methods.
• Investigate the minimum amount of data required for reliable reticle health monitoring.
• Evaluate model robustness, uncertainty, and generalization to unseen datasets.
• Present findings and recommend efficient approaches for reticle health monitoring.

This is a master's thesis or non-thesis internship for minimum 6 months, minimum 4 days per week (2 days on-site). The start date of this internship is as of February 2027.

Your profile

To be suitable for the internship, you:

• Are preferably enrolled in a master's degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Applied Mathematics, Physics, Electrical Engineering, or a related engineering field.
• Have experience with machine learning, artificial intelligence, and model development.
• Have programming experience in Python; experience with MATLAB is considered an advantage.
• Enjoy working with numerical, statistical, or spatial datasets and deriving meaningful insights from complex data.
• Are analytical, proactive, collaborative, and curious, with a strong interest in evaluating model performance and generalization.

This internship is suitable for students who want to apply AI to a real-world engineering challenge. You will work with unique industrial datasets and collaborate with engineers and data specialists to develop innovative solutions for semiconductor manufacturing. Through this internship, you will gain hands‑on experience in machine learning, data analysis, and model validation while contributing to more efficient and data‑driven reticle health monitoring. No prior knowledge of lithography or semiconductor manufacturing is required.

Other requirements you need to meet:

• You are enrolled at an educational institute for the entire duration of the internship, and you are at least 18 years old at the start of the internship;
• If you are a non-EU citizen, studying in the Netherlands, your university is willing to sign the documents relevant for doing an internship (i.e., Nuffic agreement);
• You attach your cover letter with a clear motivation on why you are interested in this internship assignment in particular;
• You need to be located in the Netherlands to be perform your internship. In case you are currently living/studying outside of the Netherlands, your application shows the willingness to relocate.

This position requires access to controlled technology, as defined in the United States Export Administration Regulations (15 C.F.R. § 730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.

Inclusion and diversity

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 inclusion and diversity is a driving force in the success of our company.

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