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Jack Wang
Rockwell Automation
Lifecycle Service
Vice President
Education
National Tsing Hua University, Engineering and System Science
Experience
· Rockwell Automation Lifecycle Service Taiwan Business Manager
· Rockwell Automation Application Development Manager
· Rockwell Automation Solution Consultant
· Rockwell Automation Senior Technical Consultant
Speech Title and Abstract
AI-Driven Semiconductor Facility Intelligence: Leveraging Advanced MPC for Energy Optimization and Sustainable Transformation
As semiconductor manufacturing advances toward sub-3nm nodes, the complexity of facility management escalates alongside energy demands. This presentation explores how integrating Artificial Intelligence with Advanced Model Predictive Control and large-scale industrial automation creates a powerful synergy for facility optimization. By shifting from reactive management to proactive, model-driven autonomous control, AI can address the "last mile" of industrial value: the real-time synchronization of facility output with actual production load. We focus on utilizing AI models to optimize energy-intensive systems such as cooling towers, chillers, and chemical distribution networks. This approach minimizes variability, reduces material waste, and significantly lowers the carbon footprint of high-tech fabs. The integration of advanced analytics with closed-loop control systems empowers semiconductor manufacturers to transform raw data into measurable sustainability, ensuring operational resilience while moving closer to Net-Zero targets.
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