Recognition of Wafer Defect Based on Two-Dimensional Principal Component Analysis Based Convolutional Autoencoder

Due to the high complexity and dynamics of the semiconductor manufacturing process, various production faults can result in various wafer defects. In order to recognize the wafer defects and trouble-shoot the root cause of the out-of-control process effectively, a novel deep neural network model, two-dimensional principal component analysis-based convolutional autoencoder(PCACAE), is proposed. Firstly, convolution kernels based on two-dimensional principal component analysis algorithm combined with prior conditions(conditional 2 DPCA, C2 DPCA) are proposed to construct the first convolutional layer of PCACAE. Secondly, the feature maps are pooled and then reconstructed, forming a convolutional encoder. Extract the coding part as weights of the second convolution layer, thus forming a well-pretrained deep network model PCACAE. Finally, fine tune the pretrained PCACAE to get the final model. PCACAE has been successfully applied to the feature learning and pattern recognition of wafer defects. PCACAE is tested in the WM-811 K database and experimental results demonstrated that PCACAE is superior to other well-known convolutional neural network models(such as GoogLeNet, DensNet, etc.) and the effectiveness was proved.