{"title":"单元内机器学习解决复杂的FinFET缺陷机制与体积扫描诊断","authors":"Manish Sharma, Yan Pan","doi":"10.31399/asm.edfa.2019-1.p004","DOIUrl":null,"url":null,"abstract":"This article presents a recent breakthrough in the use of machine learning in semiconductor FA. For the first time, cell-internal defects in FinFETs have not only been detected and diagnosed, but also refined, clarified, and resolved using cell-aware diagnosis along with root cause deconvolution (RCD) techniques. The authors describe the development of the methodology and evaluate the incremental improvements made with each step.","PeriodicalId":431761,"journal":{"name":"EDFA Technical Articles","volume":"75 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Inside the Cell to Solve Complex FinFET Defect Mechanisms with Volume Scan Diagnosis\",\"authors\":\"Manish Sharma, Yan Pan\",\"doi\":\"10.31399/asm.edfa.2019-1.p004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a recent breakthrough in the use of machine learning in semiconductor FA. For the first time, cell-internal defects in FinFETs have not only been detected and diagnosed, but also refined, clarified, and resolved using cell-aware diagnosis along with root cause deconvolution (RCD) techniques. The authors describe the development of the methodology and evaluate the incremental improvements made with each step.\",\"PeriodicalId\":431761,\"journal\":{\"name\":\"EDFA Technical Articles\",\"volume\":\"75 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EDFA Technical Articles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31399/asm.edfa.2019-1.p004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EDFA Technical Articles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31399/asm.edfa.2019-1.p004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning Inside the Cell to Solve Complex FinFET Defect Mechanisms with Volume Scan Diagnosis
This article presents a recent breakthrough in the use of machine learning in semiconductor FA. For the first time, cell-internal defects in FinFETs have not only been detected and diagnosed, but also refined, clarified, and resolved using cell-aware diagnosis along with root cause deconvolution (RCD) techniques. The authors describe the development of the methodology and evaluate the incremental improvements made with each step.