{"title":"基于非线性驱动的高效宏观模型的电源噪声建模","authors":"B. Mutnury, M. Swaminathan, J. Libous","doi":"10.1109/ISEMC.2004.1349961","DOIUrl":null,"url":null,"abstract":"In this paper, power supply noise is modeled accurately using efficient macro-models of nonlinear digital drivers. A spline function with finite time difference approximation modeling technique takes into account both the static and the dynamic memory characteristics of the driver during modeling. For power supply noise analysis, the above method has been extended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like simultaneous switching noise (SSN) and crosstalk accurately, when multiple drivers are switching simultaneously. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly nonlinear drivers, a method based on artificial neural networks (ANN) is briefly discussed to capture SSN.","PeriodicalId":378094,"journal":{"name":"2004 International Symposium on Electromagnetic Compatibility (IEEE Cat. No.04CH37559)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Modeling of power supply noise using efficient macro-model of non-linear driver\",\"authors\":\"B. Mutnury, M. Swaminathan, J. Libous\",\"doi\":\"10.1109/ISEMC.2004.1349961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, power supply noise is modeled accurately using efficient macro-models of nonlinear digital drivers. A spline function with finite time difference approximation modeling technique takes into account both the static and the dynamic memory characteristics of the driver during modeling. For power supply noise analysis, the above method has been extended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like simultaneous switching noise (SSN) and crosstalk accurately, when multiple drivers are switching simultaneously. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly nonlinear drivers, a method based on artificial neural networks (ANN) is briefly discussed to capture SSN.\",\"PeriodicalId\":378094,\"journal\":{\"name\":\"2004 International Symposium on Electromagnetic Compatibility (IEEE Cat. No.04CH37559)\",\"volume\":\"296 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 International Symposium on Electromagnetic Compatibility (IEEE Cat. No.04CH37559)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISEMC.2004.1349961\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 International Symposium on Electromagnetic Compatibility (IEEE Cat. No.04CH37559)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEMC.2004.1349961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling of power supply noise using efficient macro-model of non-linear driver
In this paper, power supply noise is modeled accurately using efficient macro-models of nonlinear digital drivers. A spline function with finite time difference approximation modeling technique takes into account both the static and the dynamic memory characteristics of the driver during modeling. For power supply noise analysis, the above method has been extended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like simultaneous switching noise (SSN) and crosstalk accurately, when multiple drivers are switching simultaneously. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly nonlinear drivers, a method based on artificial neural networks (ANN) is briefly discussed to capture SSN.