{"title":"Dynamic vs Static Burn-in for 16nm Production","authors":"Jeffrey Zhang, Antai Xu, D. Gitlin, Desmond Yeo","doi":"10.1109/IRPS45951.2020.9128338","DOIUrl":null,"url":null,"abstract":"As the automotive industry moves toward autonomous driving and zero defect, production burn-in becomes more important, so is optimizing its efficiency. Although dynamic burn-in is considered more efficient than static in theory, there have been very few reported studies based on actual data. This work analyzes production burn-in data of ~34k units produced using TSMC’s 16nm process, and shows that dynamic burn-in is approximately >4x as effective as static burn-in in catching early silicon failures","PeriodicalId":116002,"journal":{"name":"2020 IEEE International Reliability Physics Symposium (IRPS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Reliability Physics Symposium (IRPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRPS45951.2020.9128338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
As the automotive industry moves toward autonomous driving and zero defect, production burn-in becomes more important, so is optimizing its efficiency. Although dynamic burn-in is considered more efficient than static in theory, there have been very few reported studies based on actual data. This work analyzes production burn-in data of ~34k units produced using TSMC’s 16nm process, and shows that dynamic burn-in is approximately >4x as effective as static burn-in in catching early silicon failures