Intern - PEE CVD at Micron in X

Job description

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 Singapore Department Process and Equipment Engineering, Chemical Vapor Deposition (CVD) Project Title Workstation Performance Acceleration Through an Artificial Intelligence-Driven Root Cause Analysis Tool Project Description The intern will explore the application of Artificial Intelligence and machine learning to incident analysis within the Chemical Vapor Deposition process area. The project aims to transform equipment, process, and human-written incident data into structured, actionable insights that enable engineering teams to investigate recurring workstation failures more effectively. Through this project, the intern will gain exposure to semiconductor process and equipment engineering while developing and validating an AI-Enabled root cause analysis solution. The experience will involve technical learning, data analysis, model experimentation, and cross-functional engagement with production, equipment, process, hardware, and planning subject-matter experts. Objective of the Project Develop an AI-Enabled approach for interpreting human-written incident records from the Equipment Tracking Interface (ETI). Organize incident data into meaningful Pareto categories to provide clearer investigation direction. Explore correlations across equipment alarms, Fault Detection and Classification data, Statistical Process Control data, and historical incident records. Strengthen incident validation through appropriate integration with Microsoft Teams. Identify recurring failure patterns and opportunities to accelerate engineering learning cycles. Opportunities for Full-Time Employment High-performing interns may be considered for suitable full-time or future internship opportunities, subject to business needs, position availability, and the applicable selection process. Project Scope Prepare, classify, and analyze incident inputs from equipment alarms, Fault Detection and Classification systems, Statistical Process Control records, and the Equipment Tracking Interface. Explore and train Artificial Intelligence or machine learning models to interpret technical context and human-written terminology in incident logs. Develop methods for grouping incidents into Pareto categories and identifying chronic or recurring failure signatures. Evaluate an integration concept between the Equipment Tracking Interface and Microsoft Teams for incident validation. Conduct correlation studies and model validation using historical manufacturing data under the guidance of engineering subject-matter experts. Learning Opportunities Gain practical exposure to Chemical Vapor Deposition processes, semiconductor equipment engineering, process control, and workstation performance. Learn how Artificial Intelligence, machine learning, and Large Language Model techniques can be applied to unstructured engineering records. Apply root cause analysis methods such as Failure Mode and Effects Analysis, Eight Disciplines problem-solving, Pareto analysis, and Statistical Process Control. Build experience in data preparation, model simulation, validation, visualization, and the communication of technical findings. Engage with cross-functional experts from process, equipment, production, hardware, and planning functions. Deliverables A validated AI model or prototype capable of interpreting human-written Equipment Tracking Interface incident logs. A methodology for categorizing incidents into Pareto groups and identifying recurring failure patterns. A proof of concept