J. Xiong, Zaichen Chen, Yang Xiu, Z. Mu, M. Raginsky, E. Rosenbaum
{"title":"系统级ESD仿真的增强IC建模方法","authors":"J. Xiong, Zaichen Chen, Yang Xiu, Z. Mu, M. Raginsky, E. Rosenbaum","doi":"10.23919/EOS/ESD.2018.8509751","DOIUrl":null,"url":null,"abstract":"To enable accurate system-level ESD simulation, the quasi-static I-V model of an IC is enhanced through kernel regression to reflect its circuit board dependency; alternatively, a recurrent neural network may be used to generate a non-quasi-static transient model. Hybrid electromagnetic and circuit simulation is demonstrated for ESD-induced noise coupling analysis.","PeriodicalId":328499,"journal":{"name":"2018 40th Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Enhanced IC Modeling Methodology for System-level ESD Simulation\",\"authors\":\"J. Xiong, Zaichen Chen, Yang Xiu, Z. Mu, M. Raginsky, E. Rosenbaum\",\"doi\":\"10.23919/EOS/ESD.2018.8509751\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To enable accurate system-level ESD simulation, the quasi-static I-V model of an IC is enhanced through kernel regression to reflect its circuit board dependency; alternatively, a recurrent neural network may be used to generate a non-quasi-static transient model. Hybrid electromagnetic and circuit simulation is demonstrated for ESD-induced noise coupling analysis.\",\"PeriodicalId\":328499,\"journal\":{\"name\":\"2018 40th Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 40th Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/EOS/ESD.2018.8509751\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 40th Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EOS/ESD.2018.8509751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhanced IC Modeling Methodology for System-level ESD Simulation
To enable accurate system-level ESD simulation, the quasi-static I-V model of an IC is enhanced through kernel regression to reflect its circuit board dependency; alternatively, a recurrent neural network may be used to generate a non-quasi-static transient model. Hybrid electromagnetic and circuit simulation is demonstrated for ESD-induced noise coupling analysis.