Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
Job responsibilities include but are not limited to: Drive key deliverables and improvement programs for Micron internal quality metrics, including component and module‑level DPM, Product Grade, PTS, NPI qualification, reliability (WLR), GSPEC, RMA and NCD/Co NC.
Drive deep‑dive problem solving and root cause analysis, including development of systemic and specific CAPAs for RMAs, while leveraging data analytics and AI‑assisted analysis to improve efficiency and consistency. Also drive opportunities for enhanced in‑line detection and probe coverage.
Partner with Design, Product Engineering, and Quality Assurance teams to align and recommend changes to verification and validation flows, including the use of data‑driven insights, to ensure they meet Micron’s quality and reliability (QR) requirements.
Develop, debug, and support stress test programs to address highlighted reliability risks, with AI‑assisted data analysis supporting faster learning and correlation across stress conditions.
Monitor and drive improvements in quality metrics, including module production failures and customer RMAs, utilizing automated dashboards and simplified analytics workflows where applicable.
Monitor global quality dashboards and report reliability metrics not meeting specifications or performance targets.
Lead the analysis of multiple data sources (manufacturing, test, reliability, and RMA data) and ensure appropriate, timely feedback is provided to customers, supported by advanced analytics and AI‑enabled data correlation.
Proactively identify potential quality and reliability concerns, drive containment actions, and mitigate deviated material using early risk identification supported by data and analytical models.
Apply advanced reliability principles and prior product knowledge to evaluate and act on potential reliability issues, including supporting the development and refinement of reliability models with AI‑assisted data mining.
Correlate product layout with process marginality and product test coverage to identify risk areas and improvement opportunities.
Work collaboratively with Process Integration, process area, RDA, and Manufacturing teams to identify existing containment actions and drive continuous improvement, mitigating potential quality and reliability issues during the design and pre‑qualification phases of new products.
Work with other fabs on customer returns, cross‑site investigations, and lesson‑learned deployment.
Utilize Failure Mode and Effects Analysis (FMEA) and 8 D methodologies, enhanced by data and AI‑assisted insights, to continuously reduce quality and reliability failure modes.
