Multiple Granularities Generative Adversarial Network for Recognition of Wafer Map Defects

Abstract

Wafer map defect recognition (WMDR) is an important part of the integrated circuit manufacturing system. Accurate recognition of wafer map defects can help operators troubleshoot root causes of the abnormal process, and then accelerate the process adjustment. Although deep neural networks (DNNs) have been applied successfully in WMDR, class imbalance and lack of data with class labels affect their performance significantly. In view of these issues in semiconductor manufacturing processes, a new generative adversarial network (GAN), multigranularity GAN (MGGAN), is proposed for wafer map augmentation and enhancement. To alleviate instability and mode collapse of traditional GANs, the lightweight convolution and a two-way information interaction of three subnetworks are considered. MGGAN consists of an auxiliary feature extractor (AFE), a generator (G) and a discriminator (D) for wafer map generation and WMDR. First, a pretrained deep convolutional neural network (CNN), ResNet101, is employed as AFE to extract multigranularity features from wafer maps, which is used to guide the generator to reconstruct the real images. Second, in order to improve effectiveness of the adversarial training, a feature matching term is considered in the objective function of the feature generator to minimize the statistical difference between the real images and the generated images. Finally, MGGAN has been successfully applied to WMDR. The experiment results on an industrial dataset WM-811K demonstrate that MGGAN outperforms other typical GANs that aim to solve class imbalance problems and gains better recognition performance than those state-of-the-art DNNs on WMDR. This indicates effectiveness of MGGAN in image enhancement and generation. MGGAN obtains an accuracy of 88.02% on the original data and the pre-trained ResNet101 obtains 93.43% on the data enhanced by MGGAN.