High-Accuracy Mixed-Type Wafer Defect Classification Using a Custom Alex Net Architecture on the Mixed WM38 Dataset

Semiconductor wafer defect pattern recognition plays a critical role in yield management and process control within the semiconductor manufacturing industry. The identification and classification of mixed-type defects remain particularly challenging due to their complex spatial distributions and the scarcity of comprehensive datasets. This study presents a novel approach using a custom Alex Net architecture to classify the comprehensive Mixed WM38 dataset, achieving exceptional accuracy of 98.75%. The model effectively distinguishes between 38 pattern types, including single and multiple overlapping defects, enabling rapid root cause analysis in semiconductor manufacturing environments. Through comprehensive experimentation and analysis, I demonstrate how my architecture’s specific modifications address the unique challenges of wafer map classification, outperforming traditional machine learning methods and alternative deep learning architectures. The implementation demonstrates significant potential for industrial application, potentially reducing defect analysis time from hours to minutes while maintaining expert-level accuracy.