Two-Dimensional Principal Component Analysis-Based Convolutional Autoencoder for Wafer Map Defect Detection

Abstract

Due to the high complexity and dynamics of the semiconductor manufacturing process, various process abnormality could result in wafer map defects in many work stations. Thus, wafer map pattern recognition (WMPR) in the semiconductor manufacturing process can help operators to troubleshoot root causes of the out-of-control process and then accelerate the process adjustment. This article proposes a novel deep neural network (DNN), two-dimensional principal component analysis-based convolutional autoencoder (PCACAE) for wafer map defect recognition. First, a new convolution kernel based on conditional two-dimensional principal component analysis is developed to construct the first convolutional block of PCACAE. Second, a convolutional autoencoder is cascaded by considering the nonlinearity of data representation. The second convolutional block of PCACAE is constructed based on the encoding part. Finally, the pretrained PCACAE is fine-tuned to obtain the final classifier. PCACAE is successfully applied for feature learning and recognition of wafer map defects. The experimental results on a real-world case demonstrate that PCACAE is superior to other well-known convolutional neural networks (e.g., GoogLeNet, PCANet) on WMPR.