Hao Cai, Kaikai Liu, Lirida Alves de Barros Naviner
{"title":"统计变量感知方法的研究","authors":"Hao Cai, Kaikai Liu, Lirida Alves de Barros Naviner","doi":"10.1109/RFIT.2014.6933246","DOIUrl":null,"url":null,"abstract":"Conventionally circuit performance variability is analyzed with Monte-Carlo simulation and design corner analysis. On the other hand, statistical methods such as design of experiments (DoEs), response surface modeling (RSM) and compact modeling (CM) can achieve a better trade-off between simulation efficiency and accuracy. This paper investigates these variability-aware analysis methodologies. Based on industry standard BSIM4 compact model, selected physical parameters are applied to DoE-RSM and CM methods. Methodologies are validated with both analog (op-amp) and digital circuits (flip-flop) at 65 nm node. A 3X speed up is achieved with DoE-RSM. A proper selection of CM parameters is critical to model accuracy.","PeriodicalId":281858,"journal":{"name":"2014 IEEE International Symposium on Radio-Frequency Integration Technology","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A study of statistical variability-aware methods\",\"authors\":\"Hao Cai, Kaikai Liu, Lirida Alves de Barros Naviner\",\"doi\":\"10.1109/RFIT.2014.6933246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conventionally circuit performance variability is analyzed with Monte-Carlo simulation and design corner analysis. On the other hand, statistical methods such as design of experiments (DoEs), response surface modeling (RSM) and compact modeling (CM) can achieve a better trade-off between simulation efficiency and accuracy. This paper investigates these variability-aware analysis methodologies. Based on industry standard BSIM4 compact model, selected physical parameters are applied to DoE-RSM and CM methods. Methodologies are validated with both analog (op-amp) and digital circuits (flip-flop) at 65 nm node. A 3X speed up is achieved with DoE-RSM. A proper selection of CM parameters is critical to model accuracy.\",\"PeriodicalId\":281858,\"journal\":{\"name\":\"2014 IEEE International Symposium on Radio-Frequency Integration Technology\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Symposium on Radio-Frequency Integration Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RFIT.2014.6933246\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Symposium on Radio-Frequency Integration Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RFIT.2014.6933246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Conventionally circuit performance variability is analyzed with Monte-Carlo simulation and design corner analysis. On the other hand, statistical methods such as design of experiments (DoEs), response surface modeling (RSM) and compact modeling (CM) can achieve a better trade-off between simulation efficiency and accuracy. This paper investigates these variability-aware analysis methodologies. Based on industry standard BSIM4 compact model, selected physical parameters are applied to DoE-RSM and CM methods. Methodologies are validated with both analog (op-amp) and digital circuits (flip-flop) at 65 nm node. A 3X speed up is achieved with DoE-RSM. A proper selection of CM parameters is critical to model accuracy.
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