Keynote: AI-Powered Smart Quality Ecosystem: Manufacturing Next-Generation AI Memory

1:55 pm - 2:15 pm

Micron has developed an AI-driven Smart Quality Ecosystem that transforms semiconductor quality control from traditional monitoring into intelligent decision-making through three complementary AI defense lines. The first, Composite Metrology, uses AutoML ensemble models to predict measurement results for every wafer by leveraging FDC data, tool conditions, and process context, enabling earlier anomaly detection and significantly reducing post-maintenance qualification time. The second, Defect Discovery, employs MicronCraft, an AI-assisted 3D simulation platform that identifies defect origins and process sources through morphology classification and reverse simulation, reducing root-cause analysis time by approximately 20 times. The third, Trend Detection AI, replaces conventional rule-based monitoring with a self-adaptive detection engine that combines advanced statistical methods, AI yield monitoring, and evidence-based root cause diagnosis to improve detection accuracy while minimizing false alarms. Together, these three AI-powered defense lines enable earlier issue detection, faster root-cause identification, and real-time process drift monitoring, delivering measurable shift-left quality improvements and establishing AI as a trusted decision intelligence system for high-volume semiconductor manufacturing.

 

Key Technologies Covered

  • Composite Metrology: Machine-Learning model for full-lot wafers coverage from FDC and equipment data
  • Cloud-based model lifecycle management for HVM: centralized training, tuning, and real-time inference
  • Predictive SPC and smart release for first-wafer anomaly detection and fast post-PM tool recovery
  • Early drift and out-of-control detection ahead of physical inline metrology confirmation
  • AI-assisted bidirectional mechanism-based defect simulation for defect source and timing reconstruction
  • Semiconductor process digital twin with 3D visualization for virtual defect validation and knowledge transfer
  • Self-adaptive hybrid anomaly detection using rank-based and ordered trend statistical methods beyond 3-sigma rules
  • Real-time AI yield monitoring with evidence-based agentic root cause diagnosis and automated reporting

Featured Speakers

Mr. Jack Lee

Mr. Jack Lee

Senior Director, Micron Technology Taiwan, Inc.

Jack Lee is the Senior Director of Quality and Yield Enhancement at Micron OMT, with more than 20 years of experience in the semiconductor industry. Prior to joining Micron in 2016, he played a key role in the startup of Inotera's first 12-inch wafer fabrication facility and participated in the expansion of Fab 11 A/B/C. Throughout his career, he has held leadership positions across Equipment Engineering, Process Engineering, Quality Management, Cost Improvement, and Program Management, contributing to operational excellence and manufacturing performance. In addition to his operational responsibilities, Jack serves as a sponsor for Micron's Mosaic and Teams initiatives, supporting employee development, collaboration, and an inclusive workplace culture. Jack holds a master’s degree in mechanical engineering from National Chiao Tung University. Outside of work, he enjoys gardening, swimming, and watching movies and television programs.