Ensemble attention deep network for imbalanced silicon wafer defect inspection

Automated silicon wafer defect inspection is critical for sustaining yield and reliability in semiconductor manufacturing. Traditional image processing and feature engineering approaches struggle with complex and evolving defect patterns, while deep learning methods, although effective, increase computational cost and risk of overfitting. Many existing models address class imbalance through synthetic data, which can introduce artifacts and misrepresent the actual defect distributions. Additionally, conventional convolutional neural networks remain limited in capturing global context, leading to the misclassification of visually similar defects. This study addresses these issues through a simple yet effective two-stage approach. The proposed ensemble attention convolutional neural network (EACNN) first performs binary defect detection to distinguish between defective and non-defective samples, followed by a dedicated defect-type classification for fine-grained categorization. This staged design decomposes the complex multi-class problem into manageable subtasks and mitigates class dominance without synthetic data. The model further incorporates spatial and channel attention mechanisms to strengthen discriminative feature learning and improve the representation of salient defect regions. Focal loss is employed to mitigate class imbalance by down-weighting easily classified examples and placing greater emphasis on hard, misclassified samples. Optimization strategies, including batch normalization, dropout, and global average pooling, are adopted to improve generalization, reduce parameter complexity, and accelerate convergence. Experiments on the WM-811K dataset through 10-fold cross-validation demonstrated the effectiveness of EACNN and achieved 95.57% accuracy, 93.67% precision, 95.41% recall, 98.76% specificity, 94.47% F-measure, and 94.51% G-Mean. Comparisons with classical, attention-based, and imbalance-handling methods confirmed the superior performance of EACNN. The results underscore EACNN’s ability to learn highly discriminative features while preserving the integrity of real defect distributions.