Intern - Chemical Mechanical Planarization Process Engineering

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 Advanced Device Technology and Systems, Advanced Technology Enablement Dry Etch, F10 Project Title Dry Etch Process Performance Optimization Using Data Analytics and Artificial Intelligence Project Description Dry etch is a critical semiconductor manufacturing process that has a significant impact on product yield, cycle time, and manufacturing capacity. This internship project focuses on applying process engineering, data analytics, and AI-Enabled methods to understand process behavior, identify performance gaps, and develop improvement recommendations. The intern will work with manufacturing, equipment, and process datasets to explore opportunities related to process stability, productivity, and yield. The project will provide practical exposure to dry etch process engineering, industrial data analytics, data visualization, and Artificial Intelligence applications. Objective of the Project Analyze dry etch process and equipment performance using manufacturing data. Identify sources of process variation, performance drift, productivity loss, and yield impact. Develop data-driven recommendations for improving process stability

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 MSB Process Integration Engineering Project Title Semiconductor Manufacturing Reject Reduction Through Process Variability Analytics Project Description The intern will participate in a structured project focused on improving semiconductor manufacturing reject rates through process variability reduction. The project will provide practical exposure to semiconductor packaging, Assembly and Test processes, and manufacturing data analytics. Under appropriate guidance, the intern will analyze inline signals, process parameters, and test results to identify patterns and potential contributors to process variability and yield loss. The intern will also explore the responsible application of Artificial Intelligence and AI-Enabled analytical tools to improve the efficiency, quality, and effectiveness of project analysis. Objective of the Project The project aims to: Identify potential sources of process variability and yield loss. Improve the analysis of inline signals, process data, and test results. Determine relationships between process conditions and manufacturing rejects. Develop data-driven re

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 Manufacturing Wet Process Engineering (MFG WET) Project Title AI-Enabled Germhold Reduction for Dry Strip Workstation Project Description This internship project focuses on leveraging data analytics and AI-Enabled methodologies to understand and reduce equipment Germholds within the Dry Strip workstation in a semiconductor manufacturing environment. The project aims to identify key factors contributing to Germhold occurrences, develop data-driven insights, and evaluate opportunities to improve workstation efficiency and manufacturing capacity. The intern will gain hands-on experience working with operational datasets, engineering teams, and manufacturing equipment while exploring the application of Artificial Intelligence, machine learning, and predictive analytics within semiconductor manufacturing processes. Objective of the Project Identify key drivers and trends associated with equipment Germholds in the Dry Strip workstation. Apply data analytics and machine learning techniques to uncover opportunities for Germhold reduction. Develop a structured methodology to improve understanding of equipment performance and opera

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.

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 Cumulative Error Variation Reduction in Staircase and Chop Etch Processes Project Description Analyze manufacturing process and measurement data to identify sources of cumulative error variation within advanced-technology staircase and chop etch processes. Develop data-driven control strategies and process improvement recommendations to reduce variation and improve inline process stability. The project will provide exposure to dry etch processes, staircase flow integration, statistical analysis, data visualization, and the etching mechanisms relevant to semiconductor process control. Project Scope Understand the measurement methodology and process mechanisms used to define cumulative error within the staircase formation loop. Analyze process and measurement data to identify factors contributing to cumulative error variation. Apply statistical analysis and data visualization techniques to evaluate relationships between process conditions and cumulative error performance. Develop potential control strategies to reduce cumulative error variation. Recommend opportunities to improve process margins and inline stability. Learning Opportunit

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 F10 Singapore, 1 North Coast Drive Department Advanced Device Technology and Systems, Process and Equipment Engineering, Wet Process Project Title Wet Process Defectivity and Etch Rate Stability Improvement Project Description This internship project focuses on evaluating process conditions and equipment parameters to identify optimal solutions for reducing manufacturing defects, improving material etching uniformity, and enhancing process robustness within wet etch operations. The project involves structured characterization studies to develop an understanding of process and equipment behavior and evaluate techniques for reducing defectivity and process variation. Through data analysis, guided experimentation, and AI-Enabled learning, the intern will develop foundational knowledge of wet processes, equipment architecture, and semiconductor manufacturing. Objective of the Project Identify process and equipment factors that influence defectivity, etch rate uniformity, and process stability. Evaluate potential process and equipment improvements through structured characterization and data analysis. Develop recommendations that improve process robustn