{"title":"16nm制程的动态与静态老化","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":"{\"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}","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}
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
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