{"title":"Efficient PEEC-based inductance extraction using circuit-aware techniques","authors":"Haitian Hu, S. Sapatnekar","doi":"10.1109/ICCD.2002.1106808","DOIUrl":null,"url":null,"abstract":"Practical approaches for on-chip inductance extraction to obtain a sparse, stable and accurate inverse inductance matrix K are proposed. The novelty of our work is in using circuit characteristics to define the concept of resistance-dominant and inductance-dominant lines. This notion is used to progressively refine a set of clusters that are inductively tightly-coupled. For reasonable designs, the more exact algorithm yields a sparsification of 97% for delay and oscillation magnitude errors of 10% and 15%, respectively, while the more approximate algorithm achieves up to 99% sparsification. An offshoot of this work is K-PRIMA, an extension of PRIMA to handle K matrices with guaranteed passivity.","PeriodicalId":164768,"journal":{"name":"Proceedings. IEEE International Conference on Computer Design: VLSI in Computers and Processors","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Conference on Computer Design: VLSI in Computers and Processors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCD.2002.1106808","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Practical approaches for on-chip inductance extraction to obtain a sparse, stable and accurate inverse inductance matrix K are proposed. The novelty of our work is in using circuit characteristics to define the concept of resistance-dominant and inductance-dominant lines. This notion is used to progressively refine a set of clusters that are inductively tightly-coupled. For reasonable designs, the more exact algorithm yields a sparsification of 97% for delay and oscillation magnitude errors of 10% and 15%, respectively, while the more approximate algorithm achieves up to 99% sparsification. An offshoot of this work is K-PRIMA, an extension of PRIMA to handle K matrices with guaranteed passivity.