Intern - PEE CVD (AI Incident Analysis)

About the role

Micron is looking for an intern in its Process and Equipment Engineering (PEE) team for the Chemical Vapor Deposition (CVD) process area in Singapore. The project applies artificial intelligence and machine learning to incident analysis, turning equipment, process and human-written incident data into structured insights that help engineering teams investigate recurring workstation failures. High-performing interns may be considered for full-time or future internship opportunities.

Key responsibilities

• Analyze incident inputs from equipment alarms, Fault Detection and Classification systems, Statistical Process Control records and the Equipment Tracking Interface
• Explore and train AI or machine learning models to interpret technical context and human-written terminology in incident logs
• Develop methods for grouping incidents into Pareto categories and identifying recurring failure patterns
• Evaluate an integration concept between the Equipment Tracking Interface and Microsoft Teams for incident validation
• Run correlation studies and model validation with historical manufacturing data under the guidance of engineering subject-matter experts
• Deliver a validated AI model or prototype, a Pareto categorization methodology, a proof of concept for Teams integration, data visualizations and documented recommendations

Required profile

• Bachelor's or advanced degree (or equivalent) in Chemical, Electrical and Electronic, Mechanical, Materials Science, Computer, Data Science, Physics or a related field
• Ability to commit to a full-time internship of 5 months
• Interest in semiconductor manufacturing and engineering problem-solving

Required skills

• Knowledge of statistical data analysis, Design of Experiments, Statistical Process Control or root cause analysis methods is advantageous
• Familiarity with Python, structured data analysis, machine learning, Generative AI, Large Language Models or similar AI workflows is preferred
• Effective communication and collaboration with cross-functional teams