Product Yield Enhancement Engineer, HBM
Micron Technology
About the Role
Join a team responsible for bringing next-generation High Bandwidth Memory (HBM) products from design through high-volume manufacturing. This role plays a critical part in improving product reliability, accelerating production ramp, and enabling early time-to-market through advanced failure analysis.
Key Responsibilities
- Perform electrical failure analysis and fault isolation using techniques such as emission microscopy, laser-based methods, and EOTPR.
- Analyze and model failure signatures from backend testing, qualification, and RMA data.
- Apply physical failure analysis (PFA) and de-processing techniques to isolate defects.
- Develop and maintain deep technical knowledge of CMOS/FinFET fabrication processes, device structures, and process integration.
- Generate clear, organized, and timely failure analysis reports, including root cause and corrective action insights.
- Collaborate multi-functionally with Product, Process, Assembly, Quality, and Management teams to communicate findings and drive resolution.
- Apply AI-assisted tools and workflows to analyze large datasets, identify failure patterns, and accelerate root-cause determination.
- Support flexible work schedules, including swing or night shifts as required by business needs.
Requirements & Tech Stack
- Bachelor’s or Master’s degree in Electrical Engineering, Materials Science, Computer Engineering, Physics, Chemistry, or equivalent practical experience.
- 0-2 years of semiconductor industry experience in yield engineering, fault isolation, or failure analysis.
- Experience with computer-aided design (CAD), including layout and schematic analysis.
- Strong understanding of semiconductor device physics and VLSI design principles.
- Proficiency in scripting within UNIX/Linux environments (e.g., Tcl, Bash, Shell).
Qualifications
- Hands-on experience with fault isolation and failure analysis techniques.
- Strong understanding of both physical and logical semiconductor design.
- Knowledge of semiconductor packaging, assembly processes, and fabrication workflows.
- Experience in reliability engineering and quality analysis methodologies.
- Experience using AI-assisted analytics to enhance data interpretation, anomaly detection, and yield improvement initiatives.