From Reactive to Predictive: AI-Driven Optimization for ATE Performance & Reliability


As ATE systems become increasingly complex and data-intensive, traditional rule-based optimization methods struggle to keep pace. In this Semicon Korea presentation, Cohu's Wai-Kong Chen will be exploring how artificial intelligence enables a paradigm shift from reactive troubleshooting to predictive and self-optimizing ATE systems. Read more here. Fig.1: Sweet spot inference.  Sourc... » read more