Wafer Defect Detection and Classification Algorithms Based on Convolutional Neural Network

2018DOAJ (DOAJ: Directory of Open Access Journals)Engineering被引 1

摘要

For defect detection and defect classification problems of Scanning Electron Microscope (SEM) images during wafer inspection,this paper applies a Convolutional Neural Network(CNN) called ZFNet to classify wafer defects.On this basis,a patch-based CNN defect detection algorithm is proposed.For better accuracy and higher speed,another detection algorithm is proposed by modifying Faster RCNN classifier.Experimental results show that,by learning from the defects data marked with locations and types,the two detection algorithms can both detect and classify defects of multiple types on SEM images.

引用本文（GB/T 7714）

FANG Xin,SHI Zheng. Wafer Defect Detection and Classification Algorithms Based on Convolutional Neural Network[J]. DOAJ (DOAJ: Directory of Open Access Journals), 2018.

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DOI：https://doi.org/10.19678/j.issn.1000-3428.0047107

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