Pan Tian, Chen Li, Hao Fu, Xueru Yu, Zhengying Wei, Qiliang Ni, Xu Chen, Yunwei Ding, Ruojia Xu, Rui Sun
{"title":"基于DCNN模型的晶圆缺陷分类","authors":"Pan Tian, Chen Li, Hao Fu, Xueru Yu, Zhengying Wei, Qiliang Ni, Xu Chen, Yunwei Ding, Ruojia Xu, Rui Sun","doi":"10.1109/CSTIC52283.2021.9461447","DOIUrl":null,"url":null,"abstract":"Wafer defect classification is essential in semiconductor manufacturing for fast response of equipment and process stability monitoring, it is also critical for product yield management. Manual defect classification is time-consuming and prone to errors. This study presents an automatic defect classification (ADC) method based on a deep convolution neutral network (DCNN) model. The trained model has proven itself to be able to achieve defect classification performance sufficiently good to serve in the Fab.","PeriodicalId":186529,"journal":{"name":"2021 China Semiconductor Technology International Conference (CSTIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Wafer Defect Classification Based on DCNN Model\",\"authors\":\"Pan Tian, Chen Li, Hao Fu, Xueru Yu, Zhengying Wei, Qiliang Ni, Xu Chen, Yunwei Ding, Ruojia Xu, Rui Sun\",\"doi\":\"10.1109/CSTIC52283.2021.9461447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wafer defect classification is essential in semiconductor manufacturing for fast response of equipment and process stability monitoring, it is also critical for product yield management. Manual defect classification is time-consuming and prone to errors. This study presents an automatic defect classification (ADC) method based on a deep convolution neutral network (DCNN) model. The trained model has proven itself to be able to achieve defect classification performance sufficiently good to serve in the Fab.\",\"PeriodicalId\":186529,\"journal\":{\"name\":\"2021 China Semiconductor Technology International Conference (CSTIC)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 China Semiconductor Technology International Conference (CSTIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSTIC52283.2021.9461447\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 China Semiconductor Technology International Conference (CSTIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSTIC52283.2021.9461447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wafer defect classification is essential in semiconductor manufacturing for fast response of equipment and process stability monitoring, it is also critical for product yield management. Manual defect classification is time-consuming and prone to errors. This study presents an automatic defect classification (ADC) method based on a deep convolution neutral network (DCNN) model. The trained model has proven itself to be able to achieve defect classification performance sufficiently good to serve in the Fab.
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