Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network  

作　　者：程知群 胡莎 刘军 Zhang Qi-Jun 

机构地区：[1]Key Laboratory of Radio-Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou 310018, China [2]Department of Electronics, Carleton University, Ottawa, K1S 5136, Canada

出　　处：《Chinese Physics B》2011年第3期342-346,共5页中国物理B（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No. 60776052)

摘　　要：In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor （HEMT） with an artificial neural network （ANN）. The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping （neuro-SM） technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor （HEMT） with an artificial neural network （ANN）. The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping （neuro-SM） technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.

关 键 词：AlGaN/GaN high electron mobility transistor MODELING artificial neural network 

分 类 号：TN32[电子电信—物理电子学] TP183[自动化与计算机技术—控制理论与控制工程]