{"title":"差分输出驱动的增强宏建模方法","authors":"Ting Zhu, P. Franzon","doi":"10.1109/BMAS.2009.5338889","DOIUrl":null,"url":null,"abstract":"This paper presents an approach for building new compact macromodels of differential output drivers. Composed of enhanced physical-based elements, the new models are capable of capturing the important intrinsic nonlinear and dynamic characteristics of the drivers. We demonstrate the approach with two typical digital drivers, low-voltage differential signaling (LVDS) driver and pre-emphasis driver. The obtained macromodels achieve excellent accuracy in capturing behaviors at various input patterns, loading conditions and supply voltages.","PeriodicalId":169567,"journal":{"name":"2009 IEEE Behavioral Modeling and Simulation Workshop","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An enhanced macromodeling approach for differential output drivers\",\"authors\":\"Ting Zhu, P. Franzon\",\"doi\":\"10.1109/BMAS.2009.5338889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an approach for building new compact macromodels of differential output drivers. Composed of enhanced physical-based elements, the new models are capable of capturing the important intrinsic nonlinear and dynamic characteristics of the drivers. We demonstrate the approach with two typical digital drivers, low-voltage differential signaling (LVDS) driver and pre-emphasis driver. The obtained macromodels achieve excellent accuracy in capturing behaviors at various input patterns, loading conditions and supply voltages.\",\"PeriodicalId\":169567,\"journal\":{\"name\":\"2009 IEEE Behavioral Modeling and Simulation Workshop\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Behavioral Modeling and Simulation Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMAS.2009.5338889\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Behavioral Modeling and Simulation Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMAS.2009.5338889","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An enhanced macromodeling approach for differential output drivers
This paper presents an approach for building new compact macromodels of differential output drivers. Composed of enhanced physical-based elements, the new models are capable of capturing the important intrinsic nonlinear and dynamic characteristics of the drivers. We demonstrate the approach with two typical digital drivers, low-voltage differential signaling (LVDS) driver and pre-emphasis driver. The obtained macromodels achieve excellent accuracy in capturing behaviors at various input patterns, loading conditions and supply voltages.