Intern - F10 PEE WET

1100 Micron SemiAsiaOP Pte Ltd; Singapore; On-site; SGD 17,000 - 26,000; Full time

Benefits offered by this job

Mentorship by engineers

Opportunity for full-time employment

AI-enabled analytics exposure

Job summary

Micron Technology, Inc. invites applications for a full-time internship in Process Engineering focused on wafer thermal management and plasma strip optimization at our Singapore site.

You will conduct DOE studies, analyze thermal behavior, and develop data-driven recommendations to improve yield, productivity, and cost performance under the guidance of senior engineers. The program offers exposure to AI-enabled data exploration, visualization, and predictive analytics, with potential

Qualifications

• Pursuing a bachelor’s or master’s degree in chemical engineering, materials science, mechanical, electrical engineering or physics.
• Commit to a full-time internship of at least four months.
• Interest in semiconductor manufacturing and data-driven decision making.
• Familiarity with Excel; exposure to Python/JMP/Minitab/Power BI is advantageous.

Responsibilities

• Plan and conduct Design of Experiments studies to identify temperature relationships.
• Analyze wafer thermal data and relate chuck temperature to process performance.
• Develop data-based optimization recommendations to improve yield and productivity.
• Prepare technical reports and a final presentation of findings.

Skills

Strong analytical skills

Design of Experiments

Statistical analysis

Data visualization

Communication

Education

Bachelor's or Master's degree in Chemical Eng / Materials Science / Mechanical Eng / Electrical Eng / Physics

Tools

Excel

Python

JMP

Minitab

Power BI

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 F10 site, 1 North Coast Drive, Singapore 757432 Department Process Engineering Project Title PSK Thermal Optimization for Advanced Semiconductor Manufacturing

The intern will undertake a structured Process Engineering project focused on wafer thermal management and plasma strip process optimization. Under the guidance of experienced engineers, the intern will investigate the impact of chuck temperature on wafer thermal behaviour, film properties, and process performance. The project will involve experimentation, Design of Experiments, statistical analysis, process modelling, and data visualization. The intern will also gain exposure to AI-Enabled data exploration and predictive analytics for identifying process relationships and optimization opportunities. Through this project, the intern will develop practical knowledge of semiconductor manufacturing while contributing data-based recommendations for improving process robustness, yield, productivity, and cost performance.

Objective of the Project

• Develop an understanding of wafer thermal management and plasma strip processing in semiconductor manufacturing.
• Characterize the relationship between chuck temperature, wafer thermal behaviour, film properties, and process performance.
• Apply Design of Experiments and statistical analysis to identify key process-temperature relationships.
• Develop data-based optimization recommendations that may improve process robustness, yield, productivity, or cost performance.

Opportunities for Full-Time Employment

High-performing interns may be considered for future internship or full-time employment opportunities, subject to business requirements, position availability, and the applicable selection process.

Project Scope

• Study the assigned plasma strip process, equipment configuration, process parameters, and relevant thermal behaviour.
• Plan and conduct defined experiments to investigate the impact of chuck temperature on wafer thermal behaviour, film properties, and process performance.
• Develop and complete Design of Experiments studies to identify significant process-temperature relationships and optimization opportunities.
• Analyze experimental and manufacturing data using statistical techniques, process modelling, visualization, and relevant AI-Enabled workflows.
• Develop recommendations for improving process robustness, yield, productivity, or cost performance based on the project findings.

Learning Opportunities

Gain practical exposure to semiconductor manufacturing, plasma processing, and wafer thermal management. Develop experience in Design of Experiments, statistical analysis, process modelling, and data-driven decision-making. Learn how process engineers evaluate thermal behaviour, film properties, process performance, and manufacturing variation. Gain exposure to Machine Learning fundamentals, predictive analytics, Generative AI, and AI Assistants in a process-engineering context.

Deliverables

• A thermal-characterization and Design of Experiments study report.
• A documented analysis of the relationship between chuck temperature and selected process-performance indicators.
• A predictive analytics model or methodology linking thermal conditions to process performance.
• Data-based optimization recommendations and a final technical presentation.

Impact of the Project

Improve understanding of the relationship between chuck temperature, wafer thermal behaviour, and process performance. Identify potential opportunities to improve plasma strip process robustness and manufacturing consistency. Provide analytical findings that may inform future yield, productivity, and cost-improvement initiatives.

Skillsets Required

• Strong analytical, experimental, and problem-solving skills.
• Familiarity with statistical analysis, Design of Experiments, data visualization, or process modelling.
• Experience with Microsoft Excel; exposure to Python, JMP, Minitab, Power BI, or similar analytical tools is advantageous.
• Interest in semiconductor manufacturing, Machine Learning, Generative AI, and AI-Enabled data-analysis workflows.
• Ability to communicate technical findings clearly and collaborate with engineering professionals.

Course of Interest

The ideal candidate should be pursuing a bachelor’s or master’s degree in chemical engineering, Materials Science, Mechanical Engineering, Electrical Engineering, Physics, or a related engineering or scientific discipline.

Duration of Period

The ideal candidate should be able to commit to a full-time internship period of at least four months.

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 [e-mail]

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.