{"title":"机器学习在集成电路测试中的应用","authors":"H. Stratigopoulos","doi":"10.1109/ETS.2018.8400701","DOIUrl":null,"url":null,"abstract":"In recent years, a large number of works have surfaced demonstrating applications of machine learning in the field of integrated circuit testing. Many of these works showcase the effectiveness of machine learning compared to the current industry practice on actual case studies with industrial data. The aim of the paper is to offer a concise and comprehensive tutorial on machine learning applications in integrated circuit testing and to provide some practical recommendations for practitioners.","PeriodicalId":223459,"journal":{"name":"2018 IEEE 23rd European Test Symposium (ETS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"46","resultStr":"{\"title\":\"Machine learning applications in IC testing\",\"authors\":\"H. Stratigopoulos\",\"doi\":\"10.1109/ETS.2018.8400701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, a large number of works have surfaced demonstrating applications of machine learning in the field of integrated circuit testing. Many of these works showcase the effectiveness of machine learning compared to the current industry practice on actual case studies with industrial data. The aim of the paper is to offer a concise and comprehensive tutorial on machine learning applications in integrated circuit testing and to provide some practical recommendations for practitioners.\",\"PeriodicalId\":223459,\"journal\":{\"name\":\"2018 IEEE 23rd European Test Symposium (ETS)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"46\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 23rd European Test Symposium (ETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETS.2018.8400701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 23rd European Test Symposium (ETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETS.2018.8400701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In recent years, a large number of works have surfaced demonstrating applications of machine learning in the field of integrated circuit testing. Many of these works showcase the effectiveness of machine learning compared to the current industry practice on actual case studies with industrial data. The aim of the paper is to offer a concise and comprehensive tutorial on machine learning applications in integrated circuit testing and to provide some practical recommendations for practitioners.