软件导刊2024，Vol.23Issue(10)：48-54,7.DOI:10.11907/rjdk.241392
基于改进自编码器与深度特征提取器的晶圆表面缺陷检测
Wafer Surface Defect Detection Based on Improved Autoencoder and Deep Feature Extractor
凌鸿伟 1张建敏1
作者信息
- 1. 江汉大学 人工智能学院,湖北 武汉 430056
- 折叠
摘要
Abstract
In today's semiconductor defect detection field,it always faces the problem of insufficient defect samples and diversified defect samples,in order to solve the problem effectively,a wafer surface defect detection model based on improved self-encoder and deep feature ex-tractor is proposed by using the DFR model as the basic framework,which achieves a better feature extraction by using the pre-trained VGG19 model as a feature extractor,and subsequently image reconstruction using improved self-encoder to learn the normal features of the image.The anomaly scores are obtained by comparing the global differences between the input and generated images for defect detection,and the results show that for the homemade wafer dataset,the average AUC improves by 0.8 percentage points compared to the baseline model,and the accu-racy of defect detection reaches 0.997;for the MVTec AD dataset,the average AUC improves by 2.5 percentage points compared to the base-line model,and the accuracy of defect detection reaches 0.963.关键词
缺陷检测/特征提取/自编码器/AFF注意力机制Key words
defect detection/feature extraction/autoencoder/AFF attention mechanism分类
信息技术与安全科学引用本文复制引用
凌鸿伟,张建敏..基于改进自编码器与深度特征提取器的晶圆表面缺陷检测[J].软件导刊,2024,23(10):48-54,7.
