{"title":"无源降阶模型中谱零的选择","authors":"Y. Massoud, M. Alam, A. Nieuwoudt","doi":"10.1109/IWSOC.2006.348228","DOIUrl":null,"url":null,"abstract":"As process technology continues to scale into the nanoscale regime, passive components and interconnect plays an ever increasing role in realization of mixed-signal systems. In this paper, the authors develop a new method for the model order reduction of passive components and interconnect using frequency selective projection methods with interpolation points based on the spectral-zeros of the RLC interconnect model's transfer function. The methodology uses imaginary part of the spectral zeros for frequency selective adaptive projection and provides stable as well as passive reduced order models. The results indicate that our method provides more accurate approximations than techniques based on balanced truncation and moment matching","PeriodicalId":134742,"journal":{"name":"2006 6th International Workshop on System on Chip for Real Time Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":"{\"title\":\"On the Selection of Spectral Zeros for Generating Passive Reduced Order Models\",\"authors\":\"Y. Massoud, M. Alam, A. Nieuwoudt\",\"doi\":\"10.1109/IWSOC.2006.348228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As process technology continues to scale into the nanoscale regime, passive components and interconnect plays an ever increasing role in realization of mixed-signal systems. In this paper, the authors develop a new method for the model order reduction of passive components and interconnect using frequency selective projection methods with interpolation points based on the spectral-zeros of the RLC interconnect model's transfer function. The methodology uses imaginary part of the spectral zeros for frequency selective adaptive projection and provides stable as well as passive reduced order models. The results indicate that our method provides more accurate approximations than techniques based on balanced truncation and moment matching\",\"PeriodicalId\":134742,\"journal\":{\"name\":\"2006 6th International Workshop on System on Chip for Real Time Applications\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 6th International Workshop on System on Chip for Real Time Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWSOC.2006.348228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 6th International Workshop on System on Chip for Real Time Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWSOC.2006.348228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Selection of Spectral Zeros for Generating Passive Reduced Order Models
As process technology continues to scale into the nanoscale regime, passive components and interconnect plays an ever increasing role in realization of mixed-signal systems. In this paper, the authors develop a new method for the model order reduction of passive components and interconnect using frequency selective projection methods with interpolation points based on the spectral-zeros of the RLC interconnect model's transfer function. The methodology uses imaginary part of the spectral zeros for frequency selective adaptive projection and provides stable as well as passive reduced order models. The results indicate that our method provides more accurate approximations than techniques based on balanced truncation and moment matching