{"title":"Fast model generation for AlGaN/GaN HEMTs with the consideration of self-heating and charge trapping effects","authors":"Andong Huang, Z. Zhong, Yong-xin Guo, Wen Wu","doi":"10.1109/IMWS-AMP.2016.7588311","DOIUrl":null,"url":null,"abstract":"In this paper, a large signal model for AlGaN/GaN HEMTs is proposed which accounts for the thermal and trapping effects. Polynomials and overdetermined system is introduced to better address the complex thermal effect. The extraction is simple and fast compared with empirical models, since only solving overdetermined linear equations is required for the extraction of Ids. The extracted trapping and thermal related nonlinear coefficients are then represented by artificial neural network. Finally, a large signal model is constructed in Advanced Design System, and good agreements have been observed between the measured and simulated S-parameters, bias current, Pout, Gain and PAE under both 50Ohm and optimal load, which further validates the proposed model.","PeriodicalId":132755,"journal":{"name":"2016 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMWS-AMP.2016.7588311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a large signal model for AlGaN/GaN HEMTs is proposed which accounts for the thermal and trapping effects. Polynomials and overdetermined system is introduced to better address the complex thermal effect. The extraction is simple and fast compared with empirical models, since only solving overdetermined linear equations is required for the extraction of Ids. The extracted trapping and thermal related nonlinear coefficients are then represented by artificial neural network. Finally, a large signal model is constructed in Advanced Design System, and good agreements have been observed between the measured and simulated S-parameters, bias current, Pout, Gain and PAE under both 50Ohm and optimal load, which further validates the proposed model.