南京航空航天大学学报（英文版）2024，Vol.41Issue(z1)：81-88,8.DOI:10.16356/j.1005-1120.2024.S.010
基于改进ResNet的晶圆缺陷模式识别研究
Wafer Defect Map Pattern Recognition Based on Improved ResNet
摘要
Abstract
The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.关键词
ResNet/深度学习/机器视觉/晶圆缺陷模式识别Key words
ResNet/deep learning/machine vision/wafer defect map pattern recogniton分类
信息技术与安全科学引用本文复制引用
杨祎宁,魏鸿磊..基于改进ResNet的晶圆缺陷模式识别研究[J].南京航空航天大学学报（英文版）,2024,41(z1):81-88,8.基金项目
This work was supported by the 2021 Annual Scientific Research Funding Project of Liaoning Pro-vincial Department of Education(Nos.LJKZ0535,LJKZ0526)and the Natural Science Foundation of Liaoning Province(No.2021-MS-300). （Nos.LJKZ0535,LJKZ0526）
