{"title":"Middle Surface Approximtaion For Parallel And Distributed Substrate Coupling Analysis","authors":"M. A. Karami, N. Masoumi","doi":"10.1109/MIXDES.2006.1706627","DOIUrl":null,"url":null,"abstract":"In this paper a new method for accelerating substrate coupling modeling and analysis is introduced. This method is based on separating substrate to two different parts for simulation. The separation achieved by using a multi layer perceptron neural network, by approximating the middle surface of substrate for finding other points potentials. By separating the substrate, it could be simulated by different parallel processors and time of simulation in this modeling method, decreased typically by 57%. The simulation procedure distributes by applying different boundary conditions resulted from neural network approximation","PeriodicalId":318768,"journal":{"name":"Proceedings of the International Conference Mixed Design of Integrated Circuits and System, 2006. MIXDES 2006.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference Mixed Design of Integrated Circuits and System, 2006. MIXDES 2006.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIXDES.2006.1706627","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
In this paper a new method for accelerating substrate coupling modeling and analysis is introduced. This method is based on separating substrate to two different parts for simulation. The separation achieved by using a multi layer perceptron neural network, by approximating the middle surface of substrate for finding other points potentials. By separating the substrate, it could be simulated by different parallel processors and time of simulation in this modeling method, decreased typically by 57%. The simulation procedure distributes by applying different boundary conditions resulted from neural network approximation