Enhancing Defect Diagnosis and Localization in Wafer Map Testing through Weakly Supervised Learning

Defect diagnosis and localization in wafer maps are crucial tasks in semiconductor manufacturing. Existing deep learning methods often require pixel-level annotations, making them impractical for large-scale deployment. In this paper, we propose a novel weakly supervised learning approach to achieving high-precision defect identification and effective localization with only image-level labels. By leveraging the information of defect types and locations, we introduce a weighted fusion of activation maps, called Class Activation Map (CAM), to highlight class-specific regions. We further enhance defect localization accuracy and completeness by employing optimized region growing operations to eliminate noise in defect regions. Moreover, we present an optimized inference method that provides meaningful visual explanations for defect recognition. Experimental results on real-world wafer map images demonstrate the effectiveness of our approach in accurately segmenting defect patterns with no pixel-level annotations. By training the model solely on wafer map image classification labels, our proposed model significantly improves defect recognition, facilitating efficient defect analysis in semiconductor manufacturing. The proposed weakly supervised learning approach offers a practical solution for defect diagnosis and localization, with the potential of widespread adoption in the semiconductor industry.