Intern - F10 Process and Equipment Engineer

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location 1 North Coast Drive, Singapore Department F10 Process and Equipment Engineering, Chemical Vapour Deposition Module Project Title Big Data Analysis of Wafer Bevel Wafer Intelligent Scanner Metrics for Defect Reduction Project Description The Wafer Intelligent Scanner collects inspection data from the edge, or bevel, of wafers during semiconductor manufacturing. This internship project uses data analytics to study Wafer Intelligent Scanner metrics, identify patterns linked to defects, and determine early warning indicators that may help prevent future yield issues. The goal is to transform large volumes of inspection data into actionable insights that enable engineers to detect potential issues earlier and reduce wafer defects. The intern will gain practical exposure to semiconductor process and equipment engineering, defect analysis, machine learning, and AI-Enabled manufacturing analytics. Objective of the Project Identify Wafer Intelligent Scanner metrics associated with wafer defects and yield loss. Develop a data-driven methodology for detecting abnormal wafer bevel behaviour. Evaluate statistical, machine learning, and Artificial Intelligence techniques for early defect detection. Generate actionable insights and recommendations for defect reduction. Opportunities for Full Time Employment High-performing interns who demonstrate strong technical capability, learning agility, and successful project outcomes may be considered for future internship or full-time employment opportunities, subject to business needs and hiring requirements. Project Scope Gather and prepare historical Wafer Intelligent Scanner, defect, yield, process, and equipment data. Analyze and correlate Wafer Intelligent Scanner metrics with yield loss, defect occurrence, and process or equipment traces. Apply statistical, machine learning, and AI-Enabled techniques to identify abnormal Wafer Intelligent Scanner behaviour. Evaluate model performance using appropriate measures, including prediction accuracy and sensitivity. Explore Artificial Intelligence tools and AI-Enabled workflows to improve analysis, visualization, and reporting. Learning Opportunities Apply data analytics and visualization techniques to real manufacturing data. Learn about semiconductor defect analysis, yield improvement, and process monitoring. Develop practical skills in statistical analysis, machine learning, dashboard creation, and engineering problem-solving. Gain exposure to Artificial Intelligence and AI-Enabled workflows for manufacturing data analysis. Deliverables A data-driven monitoring solution using Wafer Intelligent Scanner data to identify early indicators of wafer defects. Visualizations or dashboards presenting defect trends and abnormal scanner behaviour. Model performance assessment covering prediction accuracy, sensitivity, and relevant limitations. Final report and presentation summarising the analysis, findings, and recommended actions to reduce yield loss. Impact of the Project Enable earlier identification of potential wafer defects using Wafer Intelligent Scanner data. Improve understanding of the relationship between wafer bevel metrics, process conditions, and defect occurrence. Contribute to defect-related yield-loss reduction through data-driven insights and early warning indicators. Skillsets Required Data analysis skills and familiarity with statistical or visualization tools. Structured problem-solving capabilities and a strong learning mindset. Clear technical communication and presentation skills. Familiarity with machine learning, Artificial Intelligence tools, or AI-Enabled workflows is advantageous. Course of Interest The ideal candidate should be pursuing a Degree in Chemical Engineering, Materials Science, Materials Engineering, or a related field. Duration of Period The ideal candidate should be able to commit to a full time internship period of at least 5 months from Jan 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. To request assistance with the application process and/or for reasonable accommodations, please contact hrsupport_sg@micron.com Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards. Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron. AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification. 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