Intern - F10 HVM Photo PEE

About the role

Micron is recruiting an intern for its Fab 10N/X site, working on a project titled "Photolithography Equipment Digital Twin and Throughput Optimization Using Advanced Data Analytics". The goal is to build a data-driven digital twin framework for photolithography manufacturing equipment that models tool behavior, identifies productivity constraints and evaluates optimization opportunities. The intern may be considered for full-time employment depending on business needs and availability.

Key responsibilities

• Develop data-driven digital twin models for semiconductor manufacturing equipment.
• Analyze equipment utilization, throughput, cycle time and operational performance datasets.
• Apply statistical, machine learning and simulation techniques to evaluate manufacturing scenarios and optimization opportunities.
• Build interactive dashboards and visualization tools to communicate insights and recommendations.
• Collaborate with cross-functional engineers and subject matter experts to understand manufacturing constraints.
• Use AI-enabled analytical methods to find patterns in equipment performance data and improve digital twin accuracy.
• Deliver a digital twin model, a utilization and throughput analytics dashboard, a simulation framework, a final project report and a technical presentation.

Required profile

• Pursuing a degree in Computer Science, Data Science, Data Analytics, Electrical Engineering, Mechanical Engineering, Industrial Engineering, Manufacturing Engineering, Operations Research or a related field.
• Able to commit to a full-time internship of at least 6 months.
• Strong analytical, problem-solving and communication skills.

Required skills

• Experience with Python, SQL, R or similar data analytics tools.
• Knowledge of statistics, data visualization and predictive analytics.
• Familiarity with machine learning concepts, simulation methods or data-driven workflows.
• Familiarity with AI tools or AI-enabled workflows for data analysis and predictive modeling.