基于人工神经网络的HEMT器件参数提取方法研究  

Research on HEMT device parameter extraction method based on artificial neural network

作　　者：黄兴原 秦剑[1] Huang Xingyuan;Qin Jian(Department of Electronics and Communication Engineering,Guangzhou University,Guangzhou 510006,China)

机构地区：[1]广州大学电子与通信工程系

出　　处：《电子技术应用》2020年第3期47-50,57,共5页Application of Electronic Technique

基　　金：2020年广州市科技计划基础与应用基础研究项目(202030190007)

摘　　要：研究了利用人工神经网络对不同频带、栅宽的砷化镓高电子迁移率晶体管进行散射参数和噪声参数提取,基于两个神经网络分别对两组散射参数和噪声参数进行训练学习,比较不同隐含层和神经元数目得出平均相对误差和均方误差,找到对应散射参数和噪声参数神经网络的最佳的隐含层数和神经元数目是8-8-6和6-4。测试结果表明,散射参数平均相对误差的平均值为2.79%,噪声参数平均相对误差的平均值为2.05%,与常规单个神经网络结构相比,在平均相对误差方面提高了31.3%,表明该模型具备更好的精度和可靠性,十分适用于宽禁带、强非线性特征的射频晶体管参数提取。Artificial neural network(ANN) is used to extract scattering parameters and noise parameters of GaAs high electron mobil-ity transistors with different frequency bands and gate widths.Based on the two neural networks, the two groups of scattering pa-rameters and noise parameters are trained and studied respectively.The average relative error and mean square error are obtained by comparing different hidden layers and the number of neurons.It is found that 8-8-6 and 6-4 correspond to the optimal hid-den layers and number of neurons of the neural networks with scattering parameters and noise parameters.The test results show that the average relative error of scattering parameters is 2.79 %.Compared with the conventional single neural network structure,the average relative error is increased by 31.3 %.This shows that the model in this paper has better accuracy and reliability,which shows that this model has higher accuracy and is very suitable for parameter extraction of RF transistors with wide band gap and strong nonlinearity.

关 键 词：人工神经网络 砷化镓 高电子迁移率晶体管 散射参数 噪声参数 

分 类 号：TN402[电子电信—微电子学与固体电子学]