Wafer Surface Defect Detection Based on Improved Generative Adversarial Network
For wafer surface defect detection,there are problems that the number of defect samples is insufficient and the defects are in various forms.In order to solve such problems,a wafer surface defect detection model based on improved Generative Adversarial Network is proposed.Firstly,the model introduces layer hopping connection on the basis of the GANomaly model,and introduces CBAM Attention Mechanism to better focus on the important regions of the image.Secondly,it introduces a memory module to constrain the representation of the potential space.Finally,it adds a new autoencoder architecture discriminator on the original model architecture to ensure that the training is more stable and it is easier to converge to the optimal equilibrium point.The experimental results show that the model is able to accurately distinguish wafer samples with defects,and the detection accuracy reaches 0.985,which is improved by 6.7%compared to the GANomaly algorithm.For the Mvtec AD dataset,the detection accuracy reaches 0.79,which is improved by 3%compared to the GANomaly algorithm.
万方数据
维普
