{"title":"基于主成分分析的GaN晶体管实时健康监测","authors":"F. Chalvin, Y. Miyamae, Yoshiaki Oku, K. Nakahara","doi":"10.1109/ISSM55802.2022.10026977","DOIUrl":null,"url":null,"abstract":"Adoption of next generation semiconductors is still low, partly due to limited knowledge from the reliability point of view. To help solving this problem we introduce a way to track transistor degradation in real time using PCA analysis. By using this method, it is possible to detect when a transistor is no longer operating nominally from easily obtained voltage measurements.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Principal Component Analysis Based GaN Transistor Live Health Monitoring\",\"authors\":\"F. Chalvin, Y. Miyamae, Yoshiaki Oku, K. Nakahara\",\"doi\":\"10.1109/ISSM55802.2022.10026977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Adoption of next generation semiconductors is still low, partly due to limited knowledge from the reliability point of view. To help solving this problem we introduce a way to track transistor degradation in real time using PCA analysis. By using this method, it is possible to detect when a transistor is no longer operating nominally from easily obtained voltage measurements.\",\"PeriodicalId\":130513,\"journal\":{\"name\":\"2022 International Symposium on Semiconductor Manufacturing (ISSM)\",\"volume\":\"329 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Symposium on Semiconductor Manufacturing (ISSM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSM55802.2022.10026977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSM55802.2022.10026977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Principal Component Analysis Based GaN Transistor Live Health Monitoring
Adoption of next generation semiconductors is still low, partly due to limited knowledge from the reliability point of view. To help solving this problem we introduce a way to track transistor degradation in real time using PCA analysis. By using this method, it is possible to detect when a transistor is no longer operating nominally from easily obtained voltage measurements.
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