Intern - ADTS ATI PI

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. Department ADTS ATI PI Project Title Faster Cell Metrics Drift Detection Using Advanced Data Analytics Project Description The intern will undertake a structured engineering project to develop data analytics and detection models for the early identification of cell metric drift using large-scale manufacturing test and measurement data. Under the guidance of experienced engineers, the intern will analyze relationships among inline, probe, cell metric, and quality data. The intern will gain exposure to industrial data analytics, statistical analysis, detection modelling, and visualization techniques used in semiconductor manufacturing. The project may also involve AI-Enabled workflows for data exploration, pattern identification, and analytical productivity. Project activities will focus on defined analytical assignments and measurable learning outcomes. Objective of the Project • Develop an understanding of cell metrics and their relationship with semiconductor manufacturing and product-quality data. • Analyze manufacturing data to identify early indicators of cell metric drift. • Develop a detection methodology for monitoring selected cell metrics and potential quality excursions. • Communicate analytical findings through clear dashboards, visualizations, and technical documentation. Opportunities for Full-Time Employment High-performing interns may be considered for future internship or full-time employment opportunities, subject to business requirements, position availability, and the applicable selection process. Project Scope • Study the assigned cell metrics, manufacturing data sources, and quality-monitoring requirements. • Analyze correlations among inline, probe, cell metric, and quality data. • Apply statistical analysis and relevant detection-modelling techniques to identify trends, patterns, and potential drift indicators. • Develop dashboards and visualizations to communicate analytical insights and project results. • Explore AI-Enabled workflows for data exploration, anomaly identification, or analytical productivity where relevant. Learning Opportunities • Gain hands-on experience with semiconductor manufacturing data analytics. • Develop practical knowledge of statistical analysis, detection modelling, and drift-monitoring techniques. • Learn how dashboards and visualizations are used to communicate engineering insights. • Collaborate with cross-functional engineering teams and subject matter experts. Deliverables • A documented cell metric monitoring methodology for the assigned use case. • An early drift-detection methodology and visualization solution. • A dashboard or analytical report presenting relevant trends, correlations, and findings. • Recommendations for improving cell metric and quality-monitoring effectiveness. • A final technical report and presentation summarizing the project methodology, results, limitations, and recommendations. Impact of the Project • Enable earlier detection of cell metric drift and potential quality excursions. • Improve visibility into relationships among inline, probe, cell metric, and quality data. • Provide analytical findings that may enhance product-quality monitoring and engineering decision-making. Skillsets Required • Strong analytical, problem-solving, and data-interpretation skills. • Experience with data analytics, statistical analysis, or visualization tools. • Familiarity with Python, R, Structured Query Language, Power BI, or similar analytical tools is advantageous. • Familiarity with Artificial Intelligence, AI Assistants, or AI-Enabled data-analysis workflows is advantageous. Course of Interest The ideal candidate should be pursuing a degree in Materials Science, Electrical and Electronic Engineering, Data Analytics, Statistics, Computer Science, or a related engineering or scientific discipline. Duration of Period The ideal candidate should be able to commit to a full-time internship period of at least five 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 i…