Intern - Engineer HVM PEE PHOTO

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, Fab10 Department Fab10 High-Volume Manufacturing Photo Process and Equipment Engineering Project Title Automation of Non-Zero-Offset Control for High-Volume Manufacturing Photo Processes Project Description Non-Zero-Offset is a key photo-process control parameter used in overlay-performance management and yield protection. The current workflow includes data preparation, Non-Zero-Offset generation, engineering review, validation, and post-implementation monitoring. As the number of process vintages increases, a scalable analytical approach is needed to improve workflow consistency and efficiency. The intern will undertake a structured project to develop and evaluate an automated, data-driven Non-Zero-Offset framework using Python, statistical analysis, machine learning, and visualization. The project will provide practical exposure to photo-process engineering, inline metrology, data modeling, workflow automation, and AI-Enabled engineering analysis. Objective of the Project Develop an understanding of the end-to-end Non-Zero-Offset workflow, including wafer selection, inline recipe criteria, metrology requirements, generation logic, and validation. Analyze historical Non-Zero-Offset, inline, and metrology data to identify patterns, risks, and improvement opportunities. Develop a Python-based analytical framework that improves the consistency and efficiency of Non-Zero-Offset analysis. Evaluate statistical, machine-learning, or approved AI-Assisted methods for predicting Non-Zero-Offset behavior and potential risk conditions. Opportunities for Full Time Employment Interns may be considered for future internship or full-time employment opportunities based on business requirements, role availability, project outcomes, and the applicable recruitment process. Project Scope Study the high-volume manufacturing photo-process flow and Non-Zero-Offset control methodology with relevant engineering subject matter experts. Prepare and analyze historical Non-Zero-Offset, inline, recipe, and metrology datasets using Python-based data pipelines. Develop and evaluate statistical or machine-learning approaches for identifying Non-Zero-Offset patterns, trigger conditions, and potential risk indicators. Design and prototype an automated logic flow, visualization, or dashboard that improves Non-Zero-Offset review and engineering decision-making. Learning Opportunities Gain practical exposure to photo-process engineering, overlay control, inline metrology, and semiconductor manufacturing data. Learn Python-based data preparation, statistical analysis, modeling, visualization, and workflow-automation techniques. Develop familiarity with machine learning and approved AI-Enabled tools for pattern identification, analytical interpretation, and technical documentation. Collaborate with photo-process owners and engineering subject matter experts to validate analytical results and translate findings into improvement recommendations. Deliverables A cleaned, structured, and documented dataset containing relevant Non-Zero-Offset, inline, recipe, and metrology information. Reusable Python scripts for data preparation, Non-Zero-Offset analysis, modeling, and visualization. A validated statistical or machine-learning model for Non-Zero-Offset behavior, trigger conditions, or risk identification. An automation prototype and final technical presentation covering the methodology, results, limitations, recommendations, and future scaling opportunitie