A Hybrid Model for Identifying Defect Wafer Map Patterns Based on an Optimized Deep Convolution Neural Network (OPDCNet) with a Classifier

Abstract

Identifying defective semiconductor wafers is a crucial and complicated aspect of the manufacturing process. Developed systems confront difficulties in capturing intricate defect patterns and the long-range relationship of defects. In addressing these issues, this research proposes a hybrid system that comprises a modified Mobilenet structure and the error-correcting output code (ECOC) technique-based support vector machine (SVM) classifier. In the proposed method, for feature extraction purposes, modified MobileNet architecture is designed 1) by using the Swish function instead of the ReLU function in the depth-wise separable convolution block of MobileNet structure for detecting complex defective patterns and 2) by incorporating the multi-head attention mechanism in MobileNet architecture for capturing long-range dependencies on defective wafers. The ECOC-SVM approach is used to classify wafer defects. Also, the histogram equalization technique is employed to improve the visibility of small defects. The real-world semiconductor wafer dataset WM 811K is used in this study. The proposed system with histogram equalization achieved a superior testing accuracy of 98.55% and better average values of AUC (99.74%), recall (93.34%), precision (95.64%) and F1-score (94.42%). On the other hand, the original version of the MobileNet model achieved testing accuracy of 95.31%. The proposed model correctly identified complex patterns in scratch, edge-ring, and donut types of defect wafers compared with other models by using the swish function in the model. The comparative analyses of the proposed system with other developed systems and state-of-the-art systems are given in this research.