Intern- NAND Probe Product Engineering
Micron · Singapore · 13d ago
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Location
Singapore
Department
NAND Product Engineering, Probe Product Engineering
Project Title
AI-Enabled Wafer Probe Test Optimization and Yield Analytics
Project Description
The NAND Probe Product Engineering Intern will undertake an engineering project focused on wafer probe testing, silicon characterization, yield analysis, and test optimization. Working under the guidance of experienced Product Engineers, the intern will study how probe test strategies are developed and evaluated across product development and manufacturing phases.
The project provides exposure to advanced NAND semiconductor technologies, wafer-level testing, statistical data analysis, yield improvement methodologies, and Artificial Intelligence-enabled engineering workflows. The intern will collaborate with cross-functional teams, including Design, Design for Test, Design Validation, Process Integration, Backend Test, Reliability, and Manufacturing Engineering.
Objective of the Project
Develop an understanding of wafer probe testing and its role in NAND product development and manufacturing.
Evaluate probe test data to identify yield patterns, product behavior, and test optimization opportunities.
Apply statistical analysis, automation, and Artificial Intelligence-enabled methods to an engineering problem.
Generate technical findings and recommendations that contribute to probe strategy and product engineering learning.
Opportunities for Full Time Employment
Successful interns may be considered for future internship or full-time employment opportunities based on organizational needs, role availability, academic completion, and demonstrated technical and behavioral capabilities\
Project Scope
Study wafer probe test flows, test coverage principles, screening methodologies, and product performance indicators.
Analyze probe, wafer-level, and device-level datasets to identify yield trends, anomalies, and potential failure signatures.
Participate in technical reviews involving test structures, product observability features, electrical trims, and excess schemes.
Evaluate opportunities to improve test coverage, test time, data quality, and overall test efficiency.
Develop an AI-Enabled analytical or automation solution for yield analysis, anomaly detection, data visualization, or engineering workflow improvement.
Learning Opportunities
Gain practical experience in NAND Product Engineering, wafer probe testing, and silicon characterization.
Learn how Design, Design for Test, Product Engineering, and manufacturing teams collaborate to achieve effective product test coverage.
Develop analytical and structured problem-solving skills through a defined semiconductor engineering project.
Apply Python, Structured Query Language, statistical analysis, data visualization, and Generative AI tools to engineering datasets.
Deliverables
A documented analysis of probe test data, yield trends, silicon behavior, or product performance.
Data visualizations or dashboards that communicate engineering patterns, anomalies, and findings.
An AI-Enabled analytical prototype, automation workflow, or methodology relevant to probe or yield engineering.
Technical recommendations for improving test coverage, test efficiency, yield learning, or engineering productivity.
A final technical report and presentation summarizing the project methodology, results, limitations, and recommendations.
Impact of the Project
Contribute data-driven insights for probe test strategy and product engineering evaluations.
Improve the visibility of wafer-level yield patterns and product performance indicators.
Identify potential opportunities to improve test efficiency, engineering cycle time, and manufacturing performance.
Demonstrate how Artificial Intelligence, automation, and advanced analytics can strengthen engineering decision-making.
Skillsets Required
Strong analytical, problem-solving, and technical communication skills.
Basic knowledge of semiconductor devices, digital logic, electronics, integrated circuit testing, or related engineering concepts.
Familiarity with Python, Structured Query Language, MATLAB, or equivalent programming and data analysis tools.
Familiarity with statistical analysis, data visualization, machine learning, or AI-Enabled workflows.
Willingness to learn and apply Generative AI or an AI Assistant responsibly for data exploration, code development, technical documentation, workflow automation, and engineering productivity.
Course of Interest
The ideal candidate should be pursuing a Bachelor’s, Master’s, or Doctor of Philosophy degree in Electrical Engineering, Electronics Engineering, Computer Engineering, Semiconductor Physics, Data Science with an interest in semiconductor or hardware engineering, or a related technical field.
Relevant coursework or project experience in semiconductor devices, very-large-scale integration design, digital design, data analytics, statistical analysis, machine learning, integrated circuit testing, or hardware laboratory work would be advantageous.
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 lifefor 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.
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