Intern - Engineer HVM PEE Photo (Non-Zero-Offset Automation)

About the role

Micron is looking for an intern in its Fab10 High-Volume Manufacturing Photo Process and Equipment Engineering department in Singapore. The project focuses on automating the Non-Zero-Offset control workflow, a key photo-process parameter used for overlay performance management and yield protection. The intern will build and evaluate a data-driven, Python-based framework using statistical analysis, machine learning and visualization.

Key responsibilities

• Study the high-volume manufacturing photo-process flow and Non-Zero-Offset control methodology with engineering subject matter experts.
• Analyze historical Non-Zero-Offset, inline, recipe and metrology data to identify patterns, risks and improvement opportunities.
• Develop a Python-based analytical framework and reusable scripts for data preparation, analysis, modeling and visualization.
• Evaluate statistical, machine-learning or approved AI-assisted methods for predicting Non-Zero-Offset behavior and risk conditions.
• Design and prototype an automated logic flow, visualization or dashboard that improves Non-Zero-Offset review and engineering decisions.
• Collaborate with photo-process owners to validate results and turn findings into improvement recommendations.

Required profile

• Pursuing a degree in Electrical, Electronic, Chemical, Mechanical, Industrial and Systems Engineering, Data Science, Computer Science or a related field.
• Able to commit to a full-time university internship of five months, from January 2027 to May 2027.
• Strong analytical thinking, structured problem solving and ability to work with large datasets.

Required skills

• Proficiency in Python for data preparation, analysis, modeling and visualization.
• Basic knowledge of statistics, machine learning or applied data analytics.
• Familiarity with approved AI tools or AI-enabled analytical workflows.

Interns may be considered for future internship or full-time employment based on business needs, project outcomes and the recruitment process. Deliverables include a documented dataset, Python scripts, a validated model, an automation prototype and a technical presentation.