Intern - F10 Quality – Cell Wafer Level Reliability

Overview

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build enable everything from virtual reality experiences to breakthroughs in neural networks, while committing to integrity, sustainability, and giving back to our communities.

Position Details

• Location: 1 N Coast Dr, Fab10N, Singapore
• Department: Quality Engineering Management (QEM)
• Project Title: AI‑Enabled System Intrinsic Reliability Prediction at Front-end
• Project Description: This project focuses on Cell Wafer Level Reliability (cWLR) in a semiconductor wafer fabrication environment, emphasizing fast-turn intrinsic cell reliability evaluation for process conversion, device trim assessment, and Outgoing Quality Reliability Monitoring (OQRM). The project translates system-level intrinsic reliability metrics into wafer-level proxy metrics (e.g., trigger rate and raw bit error rate, RBER) through test optimization. Machine Learning models will be applied to predict RBER across full baselines, enabling smart sampling, earlier visibility of intrinsic reliability performance at the High Volume Manufacturing (HVM) stage, and the development of a faster, scalable, and more effective intrinsic issue detection approach to safeguard production quality.
• Scope: In this project, the intern will:
  • Learn advanced NAND cell wafer-level reliability testing flows and methodologies
  • Understand semiconductor reliability failure mechanisms and device physics
  • Partner with cross-site and cross-functional teams to develop and implement cWLR test programs aligned with shift-left initiatives
  • Support NAND product characterization, experimentation, and data analysis to develop cWLR solutions for product issues
  • Apply Machine Learning techniques to model and predict NAND cell intrinsic reliability performance
  • Analyze large datasets to enable smart sampling strategies and early intrinsic risk detection
• Deliverables: The intern will be able to:
  • Understand NAND memory functions and operations
  • Gain hands-on experience in probe testing and cWLR testing
  • Develop a system-level intrinsic reliability RBER predictor using wafer-level data
  • Contribute to data-driven reliability assessment methodologies used in HVM
• Impact of Project: Improved product quality control
• Skillsets Required: Problem solving, data analytics, Python
• Course of Interest: Bachelor's/Master's Degree in Electrical/Electronic Engineering; Microelectronics preferred
• Duration: Minimum 5 months, credit-bearing full-time internship from July to November 2026

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 customers, technology leadership, and manufacturing and operational excellence, Micron delivers high-performance DRAM, NAND, and NOR memory and storage products through Micron® and Crucial® brands. Our innovations fuel the data economy, enabling advances in artificial intelligence and 5G applications that unlock opportunities—from the data center to the intelligent edge and across client and mobile experiences.

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 or hrsupport_jp@micron.com.

Compliance

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 request payment from candidates as consideration for employment.

Note on AI Use

Candidates are encouraged to use AI tools to enhance their resume and/or application materials. All information provided must be accurate and reflect true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in disqualification.

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