From Automation to Autonomy – Agentic AI for Yield Management

上午 10:05 - 上午 10:30

As wafer fabs become more complex, yield excursions increasingly arise from hidden interactions across tools and chambers, recipes, materials, carriers, maintenance events, queue time, and product-specific process sensitivity. Fabs already deploy MES, FDC, SPC, APC, recipe management, defect/yield analytics, maintenance, dispatching, and scheduling systems; however, excursion response often remains fragmented, manual, and dependent on engineers correlating signals across multiple applications.

This presentation demonstrates how semiconductor fabs can move from fragmented automation to autonomous manufacturing by implementing an agentic AI-driven closed-loop that detects yield excursions, identifies root causes, executes governed containment and recovery actions, and continuously learns to improve future outcomes.

The framework senses abnormal behavior in process, tool, metrology, defect, and yield signals; contextualizes the event through lot, wafer, tool, chamber, recipe, material, durable, maintenance, and dispatch genealogy; ranks likely root causes with confidence and evidence; identifies exposed WIP; simulates containment options and production impact; and recommends or executes governed actions through MES hold/release, OCAP, dispatch/reroute, sampling, maintenance, APC, and recipe-management workflows. The objective is not simply to add a standalone AI model, but to move from point automation toward an autonomous fab operating model that can sense, decide, act, and learn within approved business rules, audit controls, and change controls.

The use case addresses key fab outcomes: faster detection-to-containment time, reduced exposed WIP, shorter root-cause cycle time, targeted containment instead of broad tool holds, improved process recovery, reduced scrap and rework risk, and less cycle-time disruption. It also builds on prior AI recipe-tuning and virtual-metrology learning, where neural-network methods can support improved process capability in nonlinear, low-volume conditions that are difficult for traditional APC alone. By combining integrated fab context with agentic AI and governed execution, the approach provides a practical path from automation to autonomy for next-generation wafer manufacturing.

Key Technologies Covered

Agentic AI for fab yield excursion management
Integrated MES, FDC, SPC, APC, recipe, defect/yield, maintenance, and dispatch data backbone
Contextualized data lake and lot/wafer/tool/chamber/material/durable genealogy
AI anomaly detection across process, tool, metrology, defect, and yield signals
Root-cause reasoning with evidence traceability and confidence scoring
Virtual metrology and AI-assisted recipe tuning for nonlinear or low-volume process conditions
OCAP-driven governed workflow automation and human-in-the-loop decision control
Dispatch/reroute simulation and digital twin assessment of containment impact
Continuous learning, model monitoring, model retraining, and outcome feedback loops
Business rules, audit control, change control, and safe progression from advisory AI to governed autonomy

Featured Speakers

Mr. Mingyuan Zhao

Sr. Manager, Strategic Marketing, Applied Materials

Mingyuan Zhao is a Senior Manager of Strategic Marketing at Applied Materials, with 19 years of experience across the semiconductor industry. At Applied Materials, he works at the intersection of technology and strategy, helping define how AI, data integration, and automation technologies can enable next-generation autonomous manufacturing.

Mingyuan’s background spans high-volume manufacturing, semiconductor device design and process development, product engineering, product management, and strategic marketing. This breadth gives him a comprehensive perspective across the semiconductor value chain, from wafer fab operations and device technology to customer engagement and market development.

He holds a Master of Science degree from Rensselaer Polytechnic Institute in Troy, New York, USA. Prior to joining Applied Materials, Mingyuan held roles at NXP Semiconductors, Wolfspeed, and Qorvo (formerly RF Micro Devices). In these roles, he gained direct hands-on experience in wafer fabrication and worked with a range of device technologies, including silicon, silicon carbide, and gallium nitride.

Mingyuan brings a unique perspective that bridges engineering and business, combining in-depth knowledge of semiconductor devices, process technologies, and high-volume manufacturing with product strategy, customer engagement, and market development. This cross-functional background enables him to connect fab-level manufacturing challenges with smart manufacturing solutions, particularly in areas such as yield improvement, automation, process control, and AI-enabled manufacturing transformation.