基于可分离卷积和注意力机制的晶圆缺陷检测

为对半导体晶圆的表面缺陷进行快速检测,提出一种基于深度可分离卷积和注意力机制的轻量级网络,并在WM-811K数据集上进行了实验.为解决该数据集中9种不同类别的缺陷比例相对不平衡问题,采用了数据增强方法对较少数据的缺陷类别进行数据扩充.本文模型中的深度可分离卷积可以降低模型的参数量,提高模型的推理速度;注意力机制可以使模型更加关注晶圆图像中有缺陷的区域,使模型达到更好的分类效果.实验表明,所提方法在WM-811K数据集上的平均准确率高达96.5%,相对于ANN、VGG16、MobileNetv2等方法均有不同程度的提高,并且参数量和运算量只是经典轻量级网络MobileNetv2的73.5%和28.6%.

Wafer Defect Detection Based on Separable Convolution and Attention Mechanism

A lightweight network based on depthwise separable convolution and the attention mechanism is proposed for fast detection of surface defects on semiconductor wafers, and experiments are conducted on the WM-811K dataset. As the proportions of defects of nine different categories in this dataset are imbalanced, a data enhancement method is used to expand the data for defect categories with few data. The depthwise separable convolution in this model can reduce the number of parameters and improve the inference speed of the model. The attention mechanism can make the model pay more attention to the defective regions in the wafer image so that the model can achieve better classification results. The experiments show that the average accuracy of the proposed method on the WM-811K dataset is as high as 96.5%, which is improved to varying degrees compared with that of ANN, VGG16, and MobileNetv2. In addition, the number of parameters and the amount of operation are only 73.5% and 28.6% of those of the classical lightweight network MobileNetv2, respectively.