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Behavioral Model for RF Power Transistors Based on Canonical Section-Wise Piecewise Linear Functions
IEEE Transactions on Microwave Theory and Techniques ( IF 4.1 ) Pub Date : 2020-04-01 , DOI: 10.1109/tmtt.2020.2963979
Jialin Cai , Jun Liu , Jiangtao Su , Lingling Sun , Shichang Chen , Jing Xia , Justin B. King

A new, frequency-domain, behavioral modeling methodology for radio frequency (RF) power transistors, based on canonical section-wise piecewise linear (CSWPL) functions, is presented in this article. The basic theory of the proposed model for fundamental, harmonic, and dc responses is provided within. Compared with the existing nonlinear behavioral models, which require tables of coefficients to account for varying input powers, the model described in this article is able to predict transistor behavior over the entire Smith chart, at different levels of input power, from the linear region to strongly nonlinear region, with only a single set of model coefficients. Model verification is performed at fundamental, harmonic, and dc frequencies through comparisons with simulated data from a reference nonlinear circuit model and with experimental data from separate 10- and 2-W GaN HEMT devices, over a wide range of load conditions. The models can predict, accurately, the optimal area on the Smith chart for maximum fundamental output power. Furthermore, the ability of the model to interpolate across input power levels is also tested, with excellent fidelity to the simulated and measured data obtained. Compared with a 2-D polynomial-based model, the proposed method provides for a more accurate prediction, while using fewer model coefficients.

中文翻译:

基于规范分段分段线性函数的射频功率晶体管行为模型

本文介绍了一种基于规范分段分段线性 (CSWPL) 函数的射频 (RF) 功率晶体管的新频域行为建模方法。其中提供了所提出的基波、谐波和直流响应模型的基本理论。与现有的非线性行为模型(需要系数表来说明不同的输入功率)相比,本文中描述的模型能够预测整个史密斯圆图上的晶体管行为,在不同的输入功率水平下,从线性区域到强非线性区域,只有一组模型系数。模型验证在基波、谐波、通过与来自参考非线性电路模型的仿真数据以及来自不同负载条件下的 10 瓦和 2 瓦 GaN HEMT 器件的实验数据进行比较,我们可以了解直流频率和直流频率。这些模型可以准确地预测史密斯圆图上最大基波输出功率的最佳区域。此外,还测试了模型对输入功率电平进行插值的能力,对获得的模拟和测量数据具有出色的保真度。与基于二维多项式的模型相比,所提出的方法提供了更准确的预测,同时使用了更少的模型系数。还测试了模型在输入功率电平之间进行插值的能力,对获得的模拟和测量数据具有出色的保真度。与基于二维多项式的模型相比,所提出的方法提供了更准确的预测,同时使用了更少的模型系数。还测试了模型在输入功率电平之间进行插值的能力,对获得的模拟和测量数据具有出色的保真度。与基于二维多项式的模型相比,所提出的方法提供了更准确的预测,同时使用了更少的模型系数。
更新日期:2020-04-01
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