Bayesian inference‐based small‐signal modeling technique for GaN HEMTs
Bayesian inference‐based small‐signal modeling technique for GaN HEMTs
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DOI：
10.1002/mmce.21509
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Jialin Cai;J. King;Chao Yu;Lingling Sun
中科院分区：
文献类型：
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作者：
Jialin Cai;J. King;Chao Yu;Lingling Sun
本文提出了一种基于机器学习核心方法贝叶斯推理理论的氮化镓（GaN）高电子迁移率晶体管（hemt）建模新方法。高斯分布核函数用于基于贝叶斯的建模技术。将机器学习技术与传统等效电路模型拓扑相结合，提出了一种新的GaN HEMT器件小信号模型。这种新的建模方法利用了机器学习方法，同时保留了等效电路拓扑中固有的物理解释。本文对新的小信号模型进行了测试和验证，提取的模型与直流I-V曲线和S参数形式的实验数据非常吻合。该验证是在8 × 125 μm GaN HEMT上进行的，栅极特征尺寸为0.25 μm，工作条件范围很广。本文还提供了人工神经网络（ANN）模型的直流I-V曲线，并与提出的新模型进行了比较，后者显示出更准确的预测，特别是由于在人工神经网络（ANN）模型中可能观察到的I-V曲线中没有过拟合。
A new modeling methodology for gallium nitride (GaN) high‐electron‐mobility transistors (HEMTs) based on Bayesian inference theory, a core method of machine learning, is presented in this article. Gaussian distribution kernel functions are utilized for the Bayesian‐based modeling technique. A new small‐signal model of a GaN HEMT device is proposed based on combining a machine learning technique with a conventional equivalent circuit model topology. This new modeling approach takes advantage of machine learning methods while retaining the physical interpretation inherent in the equivalent circuit topology. The new small‐signal model is tested and validated in this article, and excellent agreement is obtained between the extracted model and the experimental data in the form of dc I–V curves and S‐parameters. This verification is carried out on an 8 × 125 μm GaN HEMT with a 0.25 μm gate feature size, over a wide range of operating conditions. The dc I–V curves from an artificial neural network (ANN) model are also provided and compared with the proposed new model, with the latter displaying a more accurate prediction benefiting, in particular, from the absence of overfitting that may be observed in the ANN‐derived I–V curves.