Manufacturers face rising pressure from global competition, fragmented supply chains, and increasing quality expectations. Yet, despite major innovation, many remain data‑rich but insight‑poor. Vast volumes of sensor and operational data often fail to deliver timely, actionable intelligence. This session introduces Remote Metrology powered by SAS AI and advanced analytics, using machine learning to create virtual measurements of product quality and process health. By combining equipment signals, process parameters, and statistical models, manufacturers gain “virtual eyes” across operations—enabling continuous outcome prediction, early anomaly detection, and performance optimization without relying solely on physical inspection.
This talk pulls back the curtain on the hidden complexity of semiconductor manufacturing, revealing how today’s processes rely heavily on slow, reactive measurements that can miss defects until it’s too late. It then teases a powerful shift: using machine learning and “virtual metrology” to predict quality in real time, transforming how factories detect issues and optimize performance. Through a real-world case study, the speaker shows how this approach delivers high accuracy and dramatically improves efficiency while reducing costly bottlenecks. The bigger question left hanging—if this works so well, why isn’t everyone doing it yet?—sets up a compelling reason to watch the full video.
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