Theoretical and Experimental Analysis of a CSWPL Behavioral Model for Microwave GaN Transistors Including DC Bias Voltages | IEEE Journals & Magazine | IEEE Xplore

Theoretical and Experimental Analysis of a CSWPL Behavioral Model for Microwave GaN Transistors Including DC Bias Voltages


Abstract:

In this article, a novel frequency-domain behavioral modeling approach for gallium-nitride (GaN) devices is presented. The proposed technique is based on using the canoni...Show More

Abstract:

In this article, a novel frequency-domain behavioral modeling approach for gallium-nitride (GaN) devices is presented. The proposed technique is based on using the canonical section-wise piecewise linear (CSWPL) model framework to interpolate the dc input and output bias voltages by a 2-D polynomial function. The basic theory associated with the developed model is described in detail and experimentally verified. The model is implemented in a commercial software and, then, validated through both dc and radio frequency (RF) tests with measured load-pull data from 6-W GaN devices. The achieved results demonstrate an excellent prediction capability, thereby proving the accuracy of the developed modeling methodology. Compared with the standard CSWPL model, the proposed model is able to predict the transistor behavior at different bias voltages with one single set of parameters, which greatly reduces the model complexity as well as the required extraction time. Compared with existing bias included models, the proposed solution shows accurate predictions over a wide range of input power levels and bias conditions, simultaneously. Additionally, the proposed model is utilized for a broadband power amplifier (PA) design for a further validation. The measurements carried out on the realized PA are compared with the simulations based on the proposed model. The comparison is performed at four different bias conditions. The agreement between measurements and simulations confirms the extracted model’s validity.
Page(s): 933 - 943
Date of Publication: 24 October 2023

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