Ensemble Learning for Wafer Defect Pattern Classification in the Semiconductor Industry

During semiconductor manufacturing, wafer defect patterns emerge in an uncontrolled environment, making immediate recognition challenging. To enhance the classification accuracy in pattern recognition, deep learning (DL) techniques are employed to address the issue of defective pattern identification. The inherent differences among various wafer defect types lead to imbalanced testing datasets, thereby reducing the efficiency of classification models in delivering accurate predictions. This study proposes a convolutional neural network (CNN) combined with a data transformation method at the feature extraction stage, incorporating Density-based features and Radon-based features to enhance the representation of key patterns. Subsequently, multiple classifiers are integrated using voting, bagging, and AdaBoost strategies to improve classification performance and model generalization. The primary objective is to mitigate data imbalance and selection bias by employing random sampling and assigning distinct weights to individual classifiers. The contributions of this study are threefold. First, we introduce a novel feature extraction strategy that leverages features extracted by the CNN model, Density-based features, and Radon-based features, effectively capturing structural and spatial characteristics in wafer defect patterns. Second, we propose an ensemble learning framework integrating multiple classifiers with optimized weighting mechanisms to enhance classification robustness. Third, we provide empirical evidence demonstrating that our weighted soft voting approach achieves superior performance, attaining a classification accuracy of 95.09% and an F1 score of 0.95. These results validate the effectiveness of our method in improving the reliability of wafer defect classification, which is crucial for advancing automated defect inspection in semiconductor manufacturing.