Micron (San Jose ops) · Singapore
Mechanical & Aerospace
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Location
Singapore
Department
MSB Process Integration Engineering
Project Title
Semiconductor Manufacturing Variability Reduction Using Advanced Data Analytics
Project Description
This project provides the intern with an opportunity to learn how advanced data analytics can be applied to reduce process variability and reject rates in semiconductor manufacturing.
The intern will analyze inline signals, process parameters, and test results to identify patterns associated with Assembly and Test issues. The project will provide practical exposure to semiconductor packaging processes, yield analysis, statistical methods, Artificial Intelligence, and cross-functional engineering collaboration.
Objective of the Project
The objective is to develop data-driven insights and analytical methods that:
Identify potential sources of manufacturing variability and yield loss.
Improve the analysis of inline signals, process data, and test results.
Detect relationships between process conditions and product rejects.
Recommend opportunities for improving process stability and manufacturing quality.
Explore AI-Enabled methods that enhance analytical efficiency and effectiveness.
Project Scope
The intern will be given the opportunity to:
Learn semiconductor packaging, Assembly and Test processes, and common sources of process variability and yield loss.
Analyze inline signals, process parameters, manufacturing data, and test results using Python, Structured Query Language, or equivalent analytical tools.
Apply statistical and data analytics techniques to identify patterns, correlations, and potential contributors to reject rates.
Explore AI-Enabled tools and analytical methods to improve the efficiency, quality, and effectiveness of the project analysis.
Document findings and present data-driven recommendations to engineering stakeholders.
Learning Opportunities
The intern will have the opportunity to:
Gain practical exposure to semiconductor packaging and high-volume manufacturing.
Learn how manufacturing variability and yield loss are investigated using process and test data.
Apply programming, statistical analysis, visualization, and structured problem-solving techniques.
Develop responsible proficiency in AI-Enabled tools and workflows in accordance with organizational standards.
Strengthen technical documentation, stakeholder communication, and cross-functional collaboration skills.
Deliverables
The intern is expected to deliver:
A structured analysis of relevant inline signals, process parameters, and test results.
Identification of key patterns and potential contributors associated with process variability and reject rates.
Data visualizations or analytical models that communicate findings clearly.
Documented recommendations and proposed next steps for engineering evaluation.
A final technical report and presentation covering the methodology, findings, limitations, and recommendations.
Impact of the Project
The project is intended to:
Improve visibility into manufacturing variability and sources of yield loss.
Enable more effective analysis of Assembly and Test issues.
Provide data-driven insights for process stability and quality improvement.
Strengthen the use of advanced analytics and AI-Enabled workflows in semiconductor manufacturing.
Establish reusable analytical methods for future variability-reduction projects.
Skillsets Required
The ideal candidate should possess:
Programming knowledge in Python, Structured Query Language, or equivalent analytical tools.
Strong analytical, problem-solving, systems-thinking, and critical-thinking capabilities.
Familiarity with statistics, data analysis, visualization, or AI-Enabled workflows.
The ability to learn quickly, investigate technical topics, and collaborate effectively with cross-functional teams.
Strong self-motivation, outcome orientation, interpersonal communication, and technical documentation skills.
Course of Interest
The ideal candidate should be pursuing a bachelor’s degree in Mechanical Engineering, Electrical Engineering, Chemical Engineering, Materials Engineering, Industrial Engineering, Computer Engineering, or a related engineering field, or have equivalent experience.
Duration of Period
The ideal candidate should be able to commit to a full time internship period of 5 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 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.
AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.
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