Intern - MFG WET

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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 operational efficiency.
• Explore AI-Enabled approaches for predictive identification of Germhold risks and improvement opportunities.

Opportunities for Full Time Employment
Interns demonstrating strong technical aptitude, learning agility, and successful project outcomes may be considered for future internship or full-time opportunities, subject to business needs and evaluation outcomes.
Project Scope

• Analyze manufacturing and equipment datasets to identify Germhold patterns, trends, and potential root causes.
• Apply statistical analysis, machine learning models, and data visualization techniques to evaluate Germhold occurrences and impacts.
• Collaborate with Process Engineers and Equipment Engineers to validate analytical findings and assess improvement opportunities.
• Develop dashboards or reporting tools to monitor Germhold trends and track improvement initiatives.
• Evaluate the use of Generative AI, AI Assistants, or Agentic AI solutions for automated insight generation and engineering reporting.

Learning Opportunities

• Gain hands-on experience with semiconductor manufacturing equipment and Wet Process operations.
• Learn how industrial datasets are analyzed to improve equipment and manufacturing performance.
• Develop practical skills in data analytics, machine learning, data visualization, and AI-Enabled engineering workflows.
• Collaborate with cross-functional engineering teams and subject matter experts to understand manufacturing challenges and improvement methodologies.
• Explore the application of Artificial Intelligence technologies within advanced manufacturing environments.

Deliverables

• Comprehensive analysis identifying major contributors to Germholds within the Dry Strip workstation.
• AI-Enabled or machine learning-based assessment highlighting opportunities for Germhold reduction.
• Dashboard or analytical reporting solution to monitor Germhold performance and improvement progress.
• Final presentation summarizing methodology, findings, recommendations, and potential future opportunities.

Impact of the Project

• Improve understanding of equipment Germhold behavior and contributing factors.
• Enable data-driven decision-making for workstation efficiency improvement.
• Support capacity enhancement through reduction of Germhold occurrences.
• Demonstrate the value of AI-Enabled analytics and predictive methodologies in manufacturing operations.

Skillsets Required

• Strong analytical thinking and problem-solving skills.
• Experience with Python, R, SQL, Power BI, or other data analytics and visualization tools.
• Familiarity with statistical analysis, data modeling, and machine learning concepts.
• Knowledge of Artificial Intelligence, Generative AI, AI Assistants, or AI-Enabled workflows is advantageous.
• Strong communication and collaboration skills with the ability to present insights effectively.

Course of Interest
The ideal candidate should be pursuing a Degree in Electrical Engineering, Mechanical Engineering, Chemical Engineering, Industrial Engineering, Data Science, Computer Science, Manufacturing Engineering, or a related field.
Duration of Period
The ideal candidate should be able to commit to a full time internship period of at least 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 [email protected]
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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