Intern- AMHS

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 Data-Driven Cross-Fab AMHS Move Reduction in a High-Volume Semiconductor Fab   Project Description This internship project focuses on exploring AI-enabled approaches to improve Automated Material Handling System (AMHS) efficiency in a high-volume semiconductor manufacturing environment. In Micron's Fab10 campus, wafer movement between buildings is performed through overhead transport systems and inter-building link bridges with finite transport capacity. As manufacturing demand continues to grow, cross-fab move volume has increased beyond desired targets, creating opportunities for optimization.   The intern will leverage Artificial Intelligence, Generative AI, AI Assistants, and data analytics techniques to study historical AMHS movement data, identify key drivers of cross-fab transfers, and evaluate opportunities to reduce non-value-added movements. The project will expose the intern to semiconductor manufacturing operations, logistics optimization, and data-driven decision-making.   Objective of the Project The objective of this project is to identify and categorize factors contributing to cross-fab transport demand and assess AI-enabled strategies that can improve transport efficiency, reduce unnecessary transfers, and enhance overall material movement performance.   Opportunities for Full Time Employment Successful completion of the internship may provide opportunities to be considered for future full-time roles, subject to business requirements, performance, and available openings.   Project Scope The intern will have opportunities to: Analyze historical AMHS transportation data and movement patterns. Apply AI Assistants and AI-enabled analytical methods to uncover key movement drivers. Evaluate factors such as tool distribution, scheduling practices, staging strategies, test wafer movements, and empty FOUP transfers. Develop and assess potential optimization concepts using data analysis, modelling, or simulation techniques. Generate recommendations to improve cross-fab transport efficiency and reduce non-value-added movements. Present findings and improvement opportunities to project stakeholders.   Learning Opportunities The intern will gain exposure to: Semiconductor manufacturing and AMHS operations Data analytics and visualization techniques Generative AI and AI-enabled workflows Operations modelling and simulation concepts Logistics and transport optimization Cross-functional problem solving and stakeholder engagement   Deliverables Analysis of cross-fab move demand and key contributing factors AI-enabled insights and optimization opportunities Simulation or modelling assessment (where applicable) Recommendations to improve transport efficiency and reduce unnecessary transfers Final project presentation and documentation   Impact of the Project This project aims to demonstrate how AI-enabled analytics can improve understanding of material movement behavior and support data-driven decisions that enhance AMHS performance, transport efficiency, and manufacturing productivity.   Skillsets Required Basic proficiency in Python, SQL, or data analytics tools Familiarity with Artificial Intelligence, Generative AI, AI Assistants, or AI-enabled workflows Knowledge of statistics, data visualization, or modelling techniques Strong analytical and problem-solving skills Effective communication and presentation abilities   Course of Interest The ideal candidate should be pursuing Computer Science, Data