{"title":"基于计算机视觉和机器学习的再分配层缺陷分类","authors":"Sachin Dangayach, Prayudi Lianto, S. Mishra","doi":"10.1109/EPTC50525.2020.9315117","DOIUrl":null,"url":null,"abstract":"In the semiconductor industry, defects are yield killers and the detection/classification of which can be expensive as well as time consuming. To overcome this challenge, we propose a solution involving Computer Vision Techniques and Machine Learning to accomplish defect binning procedure in typical wafer-level packaging scenario, focusing on 2um L/S redistribution layer (RDL) features. With this approach, inspection cycle time is reduced, thereby driving faster product development.","PeriodicalId":6790,"journal":{"name":"2020 IEEE 22nd Electronics Packaging Technology Conference (EPTC)","volume":"12 1","pages":"237-241"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Redistribution Layer Defect Classification Using Computer Vision Techniques And Machine Learning\",\"authors\":\"Sachin Dangayach, Prayudi Lianto, S. Mishra\",\"doi\":\"10.1109/EPTC50525.2020.9315117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the semiconductor industry, defects are yield killers and the detection/classification of which can be expensive as well as time consuming. To overcome this challenge, we propose a solution involving Computer Vision Techniques and Machine Learning to accomplish defect binning procedure in typical wafer-level packaging scenario, focusing on 2um L/S redistribution layer (RDL) features. With this approach, inspection cycle time is reduced, thereby driving faster product development.\",\"PeriodicalId\":6790,\"journal\":{\"name\":\"2020 IEEE 22nd Electronics Packaging Technology Conference (EPTC)\",\"volume\":\"12 1\",\"pages\":\"237-241\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 22nd Electronics Packaging Technology Conference (EPTC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EPTC50525.2020.9315117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 22nd Electronics Packaging Technology Conference (EPTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPTC50525.2020.9315117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Redistribution Layer Defect Classification Using Computer Vision Techniques And Machine Learning
In the semiconductor industry, defects are yield killers and the detection/classification of which can be expensive as well as time consuming. To overcome this challenge, we propose a solution involving Computer Vision Techniques and Machine Learning to accomplish defect binning procedure in typical wafer-level packaging scenario, focusing on 2um L/S redistribution layer (RDL) features. With this approach, inspection cycle time is reduced, thereby driving faster product development.