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Micron Technology

SR/STAFF ENGINEER, FECOS CPIE YE

Micron Technology
Integrated Device ManufacturingSingapore, SingaporeFull-time2 months ago
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About the role

AI summarised

Senior/Staff Engineer role in FECOS CPIE YE at Micron, focusing on yield improvement for DRAM/NAND/HBM technologies. Responsibilities include leading strategic initiatives, yield pareto benchmarking, and deploying best-known methods across global yield engineering network. Requires 3+ years of PIE experience, proficiency in data analytic tools like Yield3, JMP, Python, and strong understanding of process integration.

IDMFull-timeFront End

Key Responsibilities

  • Lead Strategic Initiatives Lead high‑impact, cross‑functional initiatives to drive yield improvement across DRAM/NAND/HBM technologies.
  • Lead the AI transformation of YMS systems in partnership with Fab and SMAI teams.
  • Provide strong technical leadership to deliver complex projects in collaboration with Fab, TD, PE, and GQ partners.
  • Yield Pareto Benchmarking / Modelling Identify actionable yield improvement opportunities through cross-fab pareto benchmarking.
  • Develop and enhance yield models for DRAM/NAND/HBM technologies.
  • Optimize and sustain methodologies to set targets for yield program (5-bucket, detractor targets).
  • Revalidate models based on actual performance.
  • BKM Deployment Lead YE SWAT team to define, align, and deploy BKMs across global YE network.
  • Drive standardization adoption of BKMs across sites and foster a unified approach to yield engineering.
  • Partner closely with site and central cross‑functional teams to ensure seamless execution of projects and initiatives.
  • Demonstrate strong team‑player mindset and the ability to unify multiple stakeholders toward common goals.
  • Work with high attention to detail while being able to operate independently when needed.

Requirements

  • 3+ years of experience in PIE; understanding of Process/Product Integration preferred.
  • YE experience is an advantage.
  • High proficiency in data analytic tools such as Yield3, JMP, Python to perform yield analysis.
  • Proficiency in Statistical/Machine Learning is a plus.
  • Strong understanding of structural and process integration concepts, including basic process‑area knowledge.
  • Knowledge of fabrication processes, probe, backend and reliability implications preferred.
  • Demonstrated ability to make sound decisions, multitask effectively, and perform root‑cause analysis via Model-Based Problem Solving approach.
  • PhD/Masters/Bachelors with relevant course of study or experience.
  • Willingness to travel as required to support business needs.
  • Strong growth mindset — committed to learning, unlearning, and relearning to stay ahead in a rapidly evolving environment.