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Singapore
Onsite
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 Equipment Engineering (PEE)
Project Title
AI-Enabled CMP Equipment Health & Breakage Risk Prediction Dashboard
Project Description
Chemical Mechanical Planarization (CMP) tool performance directly influences wafer quality, equipment reliability, and manufacturing efficiency. This project focuses on developing an AI-Enabled dashboard that integrates fab equipment and manufacturing data to identify early indicators of wafer breakage, equipment degradation, and yield excursions.
The intern will work on data exploration, predictive analytics, and visualization techniques to develop an intelligent monitoring solution that enables proactive decision-making. Through this project, the intern will gain exposure to semiconductor manufacturing equipment, reliability engineering methodologies, Artificial Intelligence applications, and data-driven engineering practices within a high-volume manufacturing environment.
Objective of the Project
Develop an AI-Enabled equipment monitoring and risk prediction dashboard capable of identifying potential CMP tool-related wafer breakage and excursion risks before significant manufacturing impact occurs.
Opportunities for Full Time Employment
High-performing candidates may be considered for future r opportunities based on business needs and performance.
Project Scope
Analyze historical equipment and manufacturing datasets associated with CMP operations.
Correlate equipment performance indicators such as Tool ID, workstation data, preventive maintenance records, and SPC violations with wafer breakage events and yield excursions.
Develop an AI-Enabled risk scoring model using historical manufacturing and equipment reliability data.
Create interactive Power BI dashboards to visualize equipment health, excursion trends, and breakage risk indicators.
Evaluate opportunities to incorporate Generative AI, AI Assistants, or Agentic AI concepts for automated reporting and engineering insights generation.
Learning Opportunities
Gain understanding of CMP equipment architecture, maintenance practices, and reliability engineering principles.
Learn how predictive analytics and Artificial Intelligence can be applied within semiconductor manufacturing environments.
Develop experience in handling large-scale manufacturing datasets and performing statistical analysis.
Build practical skills in Power BI dashboard development and engineering data visualization.
Collaborate with equipment engineers to understand real-world equipment performance challenges and continuous improvement methodologies.
Deliverables
AI-Enabled CMP Breakage Risk Index.
Equipment Health Scorecard for CMP tools.
Pareto analysis identifying key contributors to breakage and excursion risks.
Automated Top-10 High-Risk Tool Monitoring Dashboard.
Automated reporting solution providing engineering insights and early warning indicators.
Impact of the Project
Improve visibility of equipment health conditions through predictive monitoring.
Enable earlier identification of potential wafer breakage and excursion risks.
Support proactive maintenance planning and engineering decision-making.
Reduce manual effort associated with data consolidation and risk assessment.
Demonstrate the application of AI-driven solutions to improve manufacturing equipment performance.
Skillsets Required
Basic programming knowledge in Python, SQL, or similar data analysis tools.
Familiarity with data visualization platforms such as Power BI.
Understanding of statistical analysis, machine learning, or Artificial Intelligence concepts is desirable.
Experience working with large datasets and extracting meaningful insights.
Strong analytical thinking, problem-solving capability, communication skills, and willingness to learn AI-enabled engineering workflows.
Course of Interest
The ideal candidate should be pursuing a Degree in Mechanical Engineering, Electrical Engineering, Mechatronics Engineering, Computer Engineering, Data Science, Industrial Engineering, or a related discipline.
Duration of Period
The ideal candidate should be able to commit to a full time internship period of 5 months from Jan to May 2027.
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.
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