As semiconductors become more complex with 3D packaging and nanoscale features, traditional inspection methods struggle to catch all defects efficiently. The industry needs smarter ways to assure quality without slowing production. This abstract highlights an AI-driven inspection strategy that fuses machine vision with data analytics to find hidden defects in chips and advanced packages before they escalate into costly failures. The presentation will showcase how deep learning and high-resolution imaging can revolutionize semiconductor inspection. By training AI models on vast image datasets of wafers and electronic components, subtle anomalies – from microscale pattern deviations to solder voids in heterogeneous packages – can be detected automatically in-line. The speaker will draw on his experience developing optical profilometry systems and reliability tests to explain the technical setup for inline AI inspection. Attendees will see how this approach improves defect coverage and yield: catching problems earlier, reducing false positives, and enabling adaptive process control. The result is a more intelligent, efficient inspection process ready for the next generation of electronics.
