Micron Folsom · San Francisco
Mechanical & Aerospace
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Department
Advanced Technology and Solutions, Process Equipment Engineering, Chemical Mechanical Planarization
Project Title
AI-Enabled Chemical Mechanical Planarization Process Optimization and Equipment Characterization
Project Description
The intern will undertake a structured engineering project focused on Chemical Mechanical Planarization process optimization, equipment characterization, and manufacturing excellence within a semiconductor manufacturing environment.
The project will provide practical exposure to process and metrology data analysis, statistical methods, process capability assessment, material or equipment qualification, and Artificial Intelligence-Enabled engineering workflows. The intern will collaborate with cross-functional engineering teams to develop and evaluate a solution that improves process performance, yield, cost efficiency, engineering productivity, or data accessibility.
Objective of the Project
Analyze Chemical Mechanical Planarization process, metrology, equipment, and manufacturing data to identify trends and improvement opportunities.
Apply statistical and AI-Assisted methods to evaluate process stability, capability, and performance.
Develop a dashboard, analytics model, or automation solution that improves engineering productivity or data accessibility.
Generate engineering recommendations for process optimization, equipment characterization, or qualification activities.
Opportunities for Full Time Employment
Interns may be considered for future internship or full-time employment opportunities based on business requirements, available positions, and the applicable recruitment process.
Project Scope
Analyze Chemical Mechanical Planarization process, metrology, equipment, and manufacturing datasets using statistical and AI-Assisted analytical methods.
Conduct process characterization, capability assessment, and evaluation of selected materials, consumables, or equipment changes.
Investigate a defined process-performance challenge with process, equipment, quality, and manufacturing stakeholders.
Develop and evaluate an improvement that advances process stability, yield, cost efficiency, engineering productivity, or data visibility.
Learning Opportunities
Gain practical exposure to Chemical Mechanical Planarization processes, equipment, metrology, and semiconductor manufacturing integration.
Learn process characterization, statistical analysis, process capability assessment, and qualification methodologies.
Develop familiarity with Artificial Intelligence, machine learning, data visualization, automation, and AI-Enabled engineering workflows.
Collaborate with cross-functional engineering teams and communicate technical findings to relevant stakeholders.
Deliverables
A technical report covering the project objectives, methodology, analytical results, conclusions, and engineering recommendations.
Documented process-characterization, process-capability, or qualification results that inform engineering decisions.
An AI-Enabled dashboard, analytics model, or automation tool that improves engineering productivity or data accessibility.
A final presentation outlining the project outcomes, demonstrated impact, identified limitations, and future improvement opportunities.
The internal internship programme identifies project presentations to department leaders and interim or final reports and presentations as internship completion components.
Impact of the Project
Improve the visibility and interpretation of Chemical Mechanical Planarization process and equipment data.
Strengthen process characterization and data-informed engineering decision-making.
Identify opportunities to improve process stability, yield, cost efficiency, or engineering productivity.
Demonstrate a scalable AI-Enabled or automated approach for future process-engineering applications.
Skillsets Required
Strong analytical, structured problem-solving, and experimental data-interpretation skills.
Familiarity with statistical analysis, Design of Experiments, process capability assessment, or data-visualization methods.
Basic knowledge of semiconductor manufacturing, materials science, chemical processes, mechanical engineering, or equipment engineering.
Familiarity with Artificial Intelligence tools, machine learning, AI Assistants, Generative AI, or other AI-Enabled analytical workflows.
The internal template requires AI proficiency or familiarity with AI tools and AI-Enabled workflows when relevant. Its comparable quality analytics example identifies statistical analysis, machine learning, data analytics tools, and analytical problem-solving as appropriate intern skillsets.
Course of Interest
The ideal candidate should be pursuing a degree in Chemical Engineering, Materials Science and Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Data Science, Computer Science, or a related field.
Duration of Period
The ideal candidate should be able to commit to a full time internship period of 5 months form 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
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