Transistor modeling based on LM-BPNN and CG-BPNN for the GaAs pHEMT

IF 1.6 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Numerical Modelling-Electronic Networks Devices and Fields Pub Date : 2024-07-23 DOI:10.1002/jnm.3268

Qian Lin, Shuyue Yang, Ruilan Yang, Haifeng Wu

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引用次数: 0

Abstract

In order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (CG) algorithm and the Levenberg–Marquardt (LM) algorithm, the CG-BPNN and LM-BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S-parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the CG-BPNN model is better than another S-parameters when predicting ImS₁₂ with mean square error (MSE) of 7.6632e-06, while LM-BPNN model predicts ImS₁₂ with MSE of 2.4672e-06. Meanwhile, the MSE of CG-BPNN model is higher than LM-BPNN model when predicting all the S-parameters. In addition, it shows a smaller fluctuation range for the error curve of LM-BPNN model, which is more stable than the CG-BPNN model. Therefore, the double hidden layer LM-BPNN model is the better choice to characterize the small signal of GaAs pHEMT.

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基于 LM-BPNN 和 CG-BPNN 的 GaAs pHEMT 晶体管建模

针对传统器件建模过程复杂、精度低的难题，本文采用共轭梯度（CG）算法和莱文伯格-马尔卡特（LM）算法训练双隐层反向传播神经网络（BPNN），得到并分析了砷化镓（GaAs）伪高电子迁移率晶体管（pHEMT）的CG-BPNN和LM-BPNN小信号模型。首先，将 GaAs pHEMT 的散射参数（S 参数）随机分为训练集和测试集。实验结果表明，CG-BPNN 模型预测 ImS12 的均方误差（MSE）为 7.6632e-06，优于其他 S 参数；而 LM-BPNN 模型预测 ImS12 的均方误差（MSE）为 2.4672e-06。同时，在预测所有 S 参数时，CG-BPNN 模型的 MSE 都高于 LM-BPNN。此外，LM-BPNN 模型的误差曲线波动范围较小，比 CG-BPNN 模型更稳定。因此，双隐层 LM-BPNN 模型是表征砷化镓 pHEMT 小信号的更好选择。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Numerical Modelling-Electronic Networks Devices and Fields 工程技术-工程：电子与电气

CiteScore

4.60

自引率

6.20%

发文量

101

审稿时长

>12 weeks

期刊介绍： Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models. The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics. Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.