Bayesian Inference-Based Behavioral Modeling Technique for GaN HEMTs
Bayesian Inference-Based Behavioral Modeling Technique for GaN HEMTs
复制标题
基于贝叶斯推理的 GaN HEMT 行为建模技术
DOI：
10.1109/tmtt.2019.2906304
复制
发表时间：
2019
影响因子：
4.3
通讯作者：
Liu Jun
中科院分区：
文献类型：
--
作者：
Cai Jialin;King Justin B;Su Jiangtao;Yu Chao;Chen Shichang;Sun Lingling;Wang Haiqi;Liu Jun
提出了一种基于贝叶斯推理理论的氮化镓(GaN)高电子迁移率晶体管(HEMTs)频域行为建模方法。针对基于贝叶斯的建模体系，研究了几种不同的概率分布(核)函数，并通过实验测试确定了最优的核函数。将这些结果与基于人工神经网络(ANN)的另一种方法进行了比较，数据表明，所提出的方法在提高精度的同时，缓解了众所周知的ANN过拟合问题。通过与参考非线性电路模型的仿真数据以及2 W和10 W GaN HEMT器件的实验数据的比较，在广泛的负载条件下，使用所识别的最优核在基频和谐波频率下进行模型验证。该模型可以准确地预测基波输出功率在Smith图上的最佳区域和最优功率效率区域。此外，还测试了该模型在输入功率电平和输入频率之间进行内插的能力，与仿真和测量数据具有良好的保真度。
A new, frequency-domain, behavioral modeling methodology for gallium nitride (GaN) high-electron-mobility transistors (HEMTs), based on the Bayesian inference theory, is presented in this paper. Several different probability distribution (kernel) functions are examined for the Bayesian-based modeling architecture, with the optimal kernel function identified through experimental testing. These results are compared to an alternative approach based on the artificial neural networks (ANNs), with the data showing that the proposed approach demonstrates improved accuracy, while at the same time, alleviating the well-known ANN overfitting issue. Model verification is performed at the fundamental and harmonic frequencies using the identified optimal kernel, through comparisons with simulated data from a reference nonlinear circuit model, and with experimental data from separate 2- and 10-W GaN HEMT devices, over a wide range of load conditions. The models can predict accurately the optimal area of the fundamental output power on the Smith chart and the area of optimal power efficiency. Furthermore, the ability of the model to interpolate across input power levels and input frequencies is also tested, with excellent fidelity to the simulated and measured data obtained.