Intern - Engineer HVM PEE PHOTO

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Location Singapore, Fab10

Department Fab10 High-Volume Manufacturing Photo Process and Equipment Engineering

Project Title Automation of Non-Zero-Offset Control for High-Volume Manufacturing Photo Processes

Project Description Non-Zero-Offset is a key photo-process control parameter used in overlay-performance management and yield protection. The current workflow includes data preparation, Non-Zero-Offset generation, engineering review, validation, and post-implementation monitoring. As the number of process vintages increases, a scalable analytical approach is needed to improve workflow consistency and efficiency.

The intern will undertake a structured project to develop and evaluate an automated, data-driven Non-Zero-Offset framework using Python, statistical analysis, machine learning, and visualization. The project will provide practical exposure to photo-process engineering, inline metrology, data modeling, workflow automation, and AI-Enabled engineering analysis.

Objective of the Project

• Develop an understanding of the end-to-end Non-Zero-Offset workflow, including wafer selection, inline recipe criteria, metrology requirements, generation logic, and validation.

• Analyze historical Non-Zero-Offset, inline, and metrology data to identify patterns, risks, and improvement opportunities.

• Develop a Python-based analytical framework that improves the consistency and efficiency of Non-Zero-Offset analysis.

• Evaluate statistical, machine-learning, or approved AI-Assisted methods for predicting Non-Zero-Offset behavior and potential risk conditions.

Opportunities for Full Time Employment Interns may be considered for future internship or full-time employment opportunities based on business requirements, role availability, project outcomes, and the applicable recruitment process.

Project Scope

• Study the high-volume manufacturing photo-process flow and Non-Zero-Offset control methodology with relevant engineering subject matter experts.

• Prepare and analyze historical Non-Zero-Offset, inline, recipe, and metrology datasets using Python-based data pipelines.

• Develop and evaluate statistical or machine-learning approaches for identifying Non-Zero-Offset patterns, trigger conditions, and potential risk indicators.

• Design and prototype an automated logic flow, visualization, or dashboard that improves Non-Zero-Offset review and engineering decision-making.

Learning Opportunities

• Gain practical exposure to photo-process engineering, overlay control, inline metrology, and semiconductor manufacturing data.

• Learn Python-based data preparation, statistical analysis, modeling, visualization, and workflow-automation techniques.

• Develop familiarity with machine learning and approved AI-Enabled tools for pattern identification, analytical interpretation, and technical documentation.

• Collaborate with photo-process owners and engineering subject matter experts to validate analytical results and translate findings into improvement recommendations.

Deliverables

• A cleaned, structured, and documented dataset containing relevant Non-Zero-Offset, inline, recipe, and metrology information.

• Reusable Python scripts for data preparation, Non-Zero-Offset analysis, modeling, and visualization.

• A validated statistical or machine-learning model for Non-Zero-Offset behavior, trigger conditions, or risk identification.

• An automation prototype and final technical presentation covering the methodology, results, limitations, recommendations, and future scaling opportunities.

• The internal internship guide identifies predictive models, drift-detection methods, visualization solutions, recommendations, and final reports or presentations as appropriate outputs for a comparable semiconductor analytics internship.

Impact of the Project

• Reduce repetitive analytical steps within the selected Non-Zero-Offset workflow.

• Improve consistency and visibility in Non-Zero-Offset generation, review, and validation.

• Enable earlier identification of potential overlay-performance or yield-related risk conditions.

• Demonstrate a scalable analytical and automation framework for high-volume manufacturing photo processes.

Skillsets Required

• Proficiency in Python for data preparation, analysis, modeling, and visualization.

• Basic knowledge of statistics, machine learning, or applied data analytics.

• Strong analytical thinking, structured problem-solving, and ability to work with large datasets.

• Effective technical communication skills and familiarity with approved AI tools or AI-Enabled analytical workflows.

Course of Interest The ideal candidate should be pursuing a degree in Electrical Engineering, Electronic Engineering, Chemical Engineering, Mechanical Engineering, Industrial and Systems Engineering, Data Science, Computer Science, or a related field.

Duration of Period The ideal candidate should be able to commit to a full time university internship period of five months, from January 2027 to May 2027.

About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience.

To learn more, please visit micron.com/careers

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

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