{"title":"基于人工神经网络的电荷保守场效应管建模","authors":"J. King, C. Wilson","doi":"10.23919/EUMIC.2017.8230696","DOIUrl":null,"url":null,"abstract":"The paper presents a comprehensive charge modelling approach for field-effect transistor (FET) devices. For the first time an artificial neural network (ANN) is combined with the division-by-current approach to FET charge modelling. Using this technique a large-signal charge model is extracted for a 10 W GaN device from MACOM. It is shown through measurements that excellent results may be obtained using just a single gate charge function, integrated analytically from small-signal measurements.","PeriodicalId":120932,"journal":{"name":"2017 12th European Microwave Integrated Circuits Conference (EuMIC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Charge conservative FET modelling using ANNs\",\"authors\":\"J. King, C. Wilson\",\"doi\":\"10.23919/EUMIC.2017.8230696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a comprehensive charge modelling approach for field-effect transistor (FET) devices. For the first time an artificial neural network (ANN) is combined with the division-by-current approach to FET charge modelling. Using this technique a large-signal charge model is extracted for a 10 W GaN device from MACOM. It is shown through measurements that excellent results may be obtained using just a single gate charge function, integrated analytically from small-signal measurements.\",\"PeriodicalId\":120932,\"journal\":{\"name\":\"2017 12th European Microwave Integrated Circuits Conference (EuMIC)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 12th European Microwave Integrated Circuits Conference (EuMIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/EUMIC.2017.8230696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th European Microwave Integrated Circuits Conference (EuMIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EUMIC.2017.8230696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper presents a comprehensive charge modelling approach for field-effect transistor (FET) devices. For the first time an artificial neural network (ANN) is combined with the division-by-current approach to FET charge modelling. Using this technique a large-signal charge model is extracted for a 10 W GaN device from MACOM. It is shown through measurements that excellent results may be obtained using just a single gate charge function, integrated analytically from small-signal measurements.
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