Intern - F10 HVM Photo PEE

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. Project Title Photolithography Equipment Digital Twin and Throughput Optimization Using Advanced Data Analytics Project Description Develop a data-driven digital twin framework for photolithography manufacturing equipment to model tool behavior, identify productivity constraints, and evaluate optimization opportunities. The project will leverage equipment performance, process, and manufacturing datasets to create predictive simulations that improve understanding of tool utilization, cycle time drivers, and throughput performance. The project will provide exposure to advanced manufacturing analytics, simulation modeling, machine learning techniques, and data visualization applications commonly used in semiconductor manufacturing. Objective of the Project The objective of the project is to develop an analytical framework for modeling photolithography equipment behavior, evaluating productivity constraints, and identifying data-driven opportunities to improve equipment utilization, cycle time, and throughput performance. Opportunities for Full Time Employment The intern may be considered for relevant full-time employment opportunities based on business needs, position availability, and demonstrated suitability during the internship. Project Scope 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. Develop interactive dashboards and visualization tools to communicate insights and recommendations. Collaborate with cross-functional engineers and subject matter experts to understand manufacturing constraints and improvement opportunities. Apply AI-enabled analytical methods to identify patterns in equipment performance data and enhance digital twin model accuracy. Learning Opportunities Gain exposure to semiconductor photolithography equipment, manufacturing systems, and Industry 4.0 methodologies. Develop practical knowledge of advanced manufacturing analytics and equipment performance modeling. Learn applications of simulation and machine learning techniques in semiconductor manufacturing. Strengthen communication and presentation skills through engagement with cross-functional engineering teams. Deliverables Digital twin model representing photolithography equipment operational behavior. Equipment utilization and throughput analytics dashboard. Simulation framework for evaluating productivity improvement opportunities. Identification of key performance drivers and manufacturing bottlenecks. Final project report and technical presentation summarizing the methodology, findings, recommendations, and potential implementation opportunities. Impact of the Project Enhance engineering understanding of equipment productivity and cycle time drivers. Provide data-driven insights into tool utilization and throughput constraints. Identify potential opportunities to improve equipment productivity and operational efficiency. Contribute to continuous improvement initiatives for photolithography manufacturing operations. Skillsets Required Strong analytical and problem-solving skills. Experience with Python, Structured Query Language, 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. Strong communication and presentation skills. Familiarity with Artificial Intelligence tools or AI-enabled workflows for data analysis, predictive modeling, or technical documentation. Course of Interest The ideal candidate should be pursuing a degree in Computer Science, Data Science, Data Analytics, Electrical Engineering, Mechanical Engineering, Industrial Engineering, Manufacturing Engineering, Operations Research, or a related field. Duration of Period The ideal candidate should be able to commit to a full time internship period of at least 6 months. 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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