WDP-BNN: Efficient wafer defect pattern classification via binarized neural network

Wafer map defect pattern classification using convolutional neural network (CNN) has gained a lot of attention in recent years but it demands huge computation and memory cost. Therefore, a WDP-BNN framework based on the binarized neural network is proposed to reduce memory requirement by 29.70× and speed up by 1.66×. To overcome the imbalance problem and performance loss due to binarization of network, advanced data augmentation methods including (Chip Reverse, Chip Translate, Chip Combine) along with random under-sampling method have incorporated in the framework. Experimental results on the WM-811K dataset have demonstrated that the WDP-BNN model has outperformed the state-of-the-art works with the highest classification accuracy of 94.83% and the memory reduction of 1.10-25.93×.