{"title":"基于神经网络的IC晶圆缺陷检测","authors":"Arsham Abedini, M. Ehsanian","doi":"10.1109/ICM.2017.8268815","DOIUrl":null,"url":null,"abstract":"In many researches, defects are detected with using reference image. But recently, detection of defects without reference image is considered. Because Automated visual examination systems are necessary for developing in the industry, specifically when the quality of products is considered in industry [1]. Therefore, we present a novel method for detecting defects on integrated circuit based on defect features. In this paper, we classify defects with evaluating dispersion of defects based on Hough Transform.","PeriodicalId":115975,"journal":{"name":"2017 29th International Conference on Microelectronics (ICM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Defect detection on IC wafers based on neural network\",\"authors\":\"Arsham Abedini, M. Ehsanian\",\"doi\":\"10.1109/ICM.2017.8268815\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many researches, defects are detected with using reference image. But recently, detection of defects without reference image is considered. Because Automated visual examination systems are necessary for developing in the industry, specifically when the quality of products is considered in industry [1]. Therefore, we present a novel method for detecting defects on integrated circuit based on defect features. In this paper, we classify defects with evaluating dispersion of defects based on Hough Transform.\",\"PeriodicalId\":115975,\"journal\":{\"name\":\"2017 29th International Conference on Microelectronics (ICM)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 29th International Conference on Microelectronics (ICM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICM.2017.8268815\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 29th International Conference on Microelectronics (ICM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICM.2017.8268815","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Defect detection on IC wafers based on neural network
In many researches, defects are detected with using reference image. But recently, detection of defects without reference image is considered. Because Automated visual examination systems are necessary for developing in the industry, specifically when the quality of products is considered in industry [1]. Therefore, we present a novel method for detecting defects on integrated circuit based on defect features. In this paper, we classify defects with evaluating dispersion of defects based on Hough Transform.