SAP-Integrated Yield Analytics Framework for Wafer Fabrication Using Real-Time Defect Pattern Mining

Semiconductor wafer fabrication requires fast yield control because small defect events can spread across stages and reduce final production performance. As fabrication systems become more data-intensive, manufacturers need analytical frameworks that connect defect behavior, yield movement, and enterprise-level response in real time. Recent studies have improved wafer defect recognition, low-yield diagnosis, similarity-based wafer analytics, and machine-learning-based yield prediction. However, most existing work still treats defect mining, yield interpretation, and enterprise integration as separate tasks, which limits practical use in fabrication control. To address this gap, this article presents an SAP-integrated yield analytics framework for wafer fabrication using real-time defect pattern mining. The framework synchronizes wafer maps, inspection records, process traces, and yield indicators, mines recurring and abnormal defect patterns, links them with yield relevance, and routes the resulting signals into an SAP-based monitoring and alert structure. The results show that the framework can track yield trends across wafer lots, improve defect cluster interpretation across process stages, support alert-driven yield recovery, and remain stable under increasing wafer volume, defect density, and data velocity. These findings show that the proposed framework provides a more practical basis for real-time yield intelligence in advanced wafer fabrication environments.