An Artificial Neural Network Based Nonlinear Behavioral Model for RF Power Transistors

被引：0

作者：

Cai, Jialin ^{[1
]}

Wang, Jie ^{[1
]}

Yu, Chao ^{[2
]}

Lu, Haiyan ^{[3
]}

Liu, Jun ^{[1
]}

Sun, Lingling ^{[1
]}

机构：

[1] Hangzhou Dianzi Univ, Key Lab RF Circuit & Syst, Minist Educ, Hangzhou, Zhejiang, Peoples R China

[2] Southeast Univ, State Key Lab Millimeter Waves, Nanjing, Jiangsu, Peoples R China

[3] Nanjing Elect Devices Inst, Sci & Technol Monolith Integrated Circuits & Modu, Nanjing, Jiangsu, Peoples R China

来源：

2017 IEEE ASIA PACIFIC MICROWAVE CONFERENCE (APMC) | 2017年

关键词：

Artificial neural network; nonlinear behavioral modeling; RE power transistor;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a frequency domain, nonlinear, behavioral model for RF power transistors, based on an artificial neural network (ANN), is proposed and validated. The model is identified using the back-propagation algorithm from the incident and scattered wave data of the RF transistor. The model has been extracted and validated on Cree GaN HEMT device. Both simulation and measurement examples are presented. Compared with existing nonlinear transistor behavioral models, which are all input-power dependent, the new model is able to effectively predict the behavior of a transistor over the entire Smith chart, at different levels of input power with only a single set of model coefficients.

引用

页码：600 / 603

页数：4

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