Improved Wafer Map Inspection Using Attention Mechanism and Cosine Normalization

Abstract

Wafer map inspection is essential for semiconductor manufacturing quality control and analysis. The deep convolutional neural network (DCNN) is the most effective algorithm in wafer defect pattern analysis. Traditional DCNNs rely heavily on high quality datasets for training. However, obtaining balanced and sufficient labeled data is difficult in practice. This paper reconsiders the causes of the imbalance and proposes a deep learning method that can learn robust knowledge from an imbalanced dataset using the attention mechanism and cosine normalization. We interpret the dataset imbalance as both a feature and a quantity distribution imbalance. To compensate for feature distribution imbalance, we add an improved convolutional attention module to the DCNN to enhance representation. In particular, a feature-map-specific direction mapping module is developed to amplify the positional information of defect clusters. For quantity distribution imbalance, the cosine normalization algorithm is proposed to replace the fully connected layer, and classifier fine-tuning is realized through a small amount of iterative training, which decreases the sensitivity to the quantitative distribution. The experimental results on real-world datasets demonstrate that the proposed method significantly improves the robustness of wafer map inspection and outperforms existing algorithms when trained on imbalanced datasets.