A Deep convolutional neural network with residual blocks for wafer map defect pattern recognition

Different deep convolution neural network (DCNN) models have been proposed for wafer map pattern identification and classification tasks in previous studies. However, factors such as input image resolution effect on the classification performance of the proposed models and class imbalance in the training set after splitting the data into training and test sets have not been considered in the previous studies. We propose a DCNN model with residual blocks, called the Opt-ResDCNN model, for wafer map defect pattern classification by considering 26 x 26, 64 x 64, 96 x 96, and 256 x 256 input images and class imbalance issues. The model with a balance function can improve the performance. We compare the proposed model with the published defect pattern classification models in terms of accuracy, precision, recall, and F1 value. Using a publicly available wafer map data set (WM-811K), the proposed method on the four different resolutions can obtain an excellent average accuracy, precision, recall, and F1 score. Regarding accuracy, the proposed model results are 99.90%, 99.86%, 90.28%, 98.88%, respectively. These results are better than the published papers.