Sample-imbalanced wafer map defects classification based on auxiliary classifier denoising diffusion probability model

Abstract

This paper research on the classification task of wafer map defects under imbalanced sample. To improve the number of imbalanced wafer maps, a denoising diffusion probabilistic model with auxiliary classifier is proposed to generate new wafer maps. The developed model has an U-net structure with multiple inputs and outputs, taking wafer images, noise degree and category features as inputs, and outputs predicted noise and prediction category. When performing data augmentation, the trained model gradually removes prediction noise from the initial random noise and obtains new wafers. Finally, the balanced wafer defect maps are classified using a residual neural network. The proposed auxiliary classifier denoising diffusion probabilistic model and residual neural networks (ACDDPM-ResNet) method has validated on the MIR-WM811K dataset and MixedWM38 dataset, and the defect classification results of the imbalanced wafer map are remarkable improved after data augmentation. In addition, the paper also discusses and analyzes the influence of the maximum noise addition steps and the data augmentation size on the accuracy of wafer classification. It is further validated that the proposed wafer map classification method based on data augmentation can solve the classification problem caused by sample imbalance.