Automatic Recommendation of Patch Regions for Wafer Wobbling Detection in Semiconductor Manufacturing

다국어 초록 (Multilingual Abstract)

In semiconductor manufacturing, the Clean and Coat (C&C) process critically influences product yield, yet its internal operation is difficult to observe directly. To address this limitation, we propose a patch-based region segmentation and recommendation framework for analyzing camera sensor images captured during C&C operations. The proposed method divides monitoring images into uniform patch regions suitable for AI-based image analysis and automatically recommends regions of interest that are most sensitive to abnormal phenomena such as wafer wobbling. A client–server monitoring system was implemented, where the frontend provides live or recorded video playback and the backend performs patch analysis using a FastAPI-based module.
Experimental validation using a mock-up setup demonstrated that the system can accurately detect wafer boundaries, group surface brightness levels, and rank candidate patches in real time. The proposed framework effectively reduces operator dependence, improves consistency in region-of-interest configuration, and enhances the reliability of real-time C&C process monitoring. This study provides a foundation for integrating intelligent vision algorithms into semiconductor manufacturing environments.