Abstract
This paper presents a Multi-Stage AI-Driven Total Solution designed to resolve inspection bottlenecks in semiconductor High-Volume Manufacturing. By integrating In-line ADC+, Post Micro ADC, and Whole Wafer (Macro) ADC, the system strategically augments rule-based AOI to filter optical artifacts in real-time and identify process-level patterns. Deployment results demonstrate a 95% reduction in nuisance defects caused by illumination artifacts (e.g., fluorescence noise) and 100% accuracy in wafer-level pattern classification. This unified ecosystem ensures data continuity from acquisition to review, significantly enhancing throughput and yield learning without compromising the sensitivity of optical inspections.
Event Details
| Date | Apr 15, 2026 |
|---|---|
| Time | 5:45 pm - 6:30 pm |
| Location | Strasbourg, France |
| Event | SPIE Photonics Europe |
| Presenters |
Bryce Chi
Bryce is an AI Engineer at Onto Innovation, focusing on machine learning, computer vision, and advanced analytics for semiconductor inspection systems. He works on integrating AI-driven defect detection and review automation into production workflows at leading foundries. His experience spans model development, deployment on high-volume manufacturing tools, and data engineering for large-scale wafer analysis. He is passionate about bridging innovation and practical application in the fab environment, accelerating yield improvement and enabling next-generation manufacturing intelligence.