Sample-imbalanced wafer map defects classification based on Jacobian regularized generative adversarial network

Abstract Wafer defect classification is a key component in the wafer manufacturing process. Under stable operating conditions and sufficient test data, an effective wafer defect classification model can help engineers quickly and accurately judge and solve problems in the production process. However, the complexity of the production process leads to serious imbalance between various types of defects, which greatly reduces the performance of traditional defect classification method. This paper proposes a Jacobi regularized generative adversarial network (JRGAN) for sample imbalanced wafer image defect generation. The JRGAN architecture includes a generator, a discriminator, a Jacobi regularization term, and an auxiliary classifier. The model takes random noise and sample labels as input, and integrates the Jacobi regularization term into the generator to minimize the statistical difference between the generated image and the real image. The regularization term in the discriminator improves the robustness of the network training process. This paper uses the MIR-WM811K and MixedWM38 datasets collected from real factories to verify the effectiveness of the JRGAN model proposed in this paper on the residual neural network (ResNet). Experimental results show that the proposed method can improve the quality of generated samples and improve the accuracy of wafer defect classification. The defect classification accuracy in the MIR-WM811K and MixedWM38 datasets is 97.14% and 97.38%, which is 2.21% and 0.29% higher than that of the original datasets.