J. E. Suseno, M. Riyadi, N. Alias, Y. W. Heong, R. Ismail
{"title":"Artificial intelligence techniques for SPICE optimization of MOSFET modeling","authors":"J. E. Suseno, M. Riyadi, N. Alias, Y. W. Heong, R. Ismail","doi":"10.1109/CITISIA.2009.5224238","DOIUrl":null,"url":null,"abstract":"This paper proposes new method for optimize and verified electric characterization graph of MOSFET by using artificial neural network. Optimization using Neural Network (ONN) will compare current-voltage (I–V) Characteristic graph between the TCAD simulation and TSPICE modeling as desire data control a model parameter of BSIM. In this paper, the neural network method is dynamic feedforward Neural Network. After NN training, the best result is at Neural Network architecture of 36-30-10-5 with Mean Squared Error (MSE) of 1e-28 at epoch of 5.","PeriodicalId":144722,"journal":{"name":"2009 Innovative Technologies in Intelligent Systems and Industrial Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Innovative Technologies in Intelligent Systems and Industrial Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CITISIA.2009.5224238","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
This paper proposes new method for optimize and verified electric characterization graph of MOSFET by using artificial neural network. Optimization using Neural Network (ONN) will compare current-voltage (I–V) Characteristic graph between the TCAD simulation and TSPICE modeling as desire data control a model parameter of BSIM. In this paper, the neural network method is dynamic feedforward Neural Network. After NN training, the best result is at Neural Network architecture of 36-30-10-5 with Mean Squared Error (MSE) of 1e-28 at epoch of 5.