Abstract
A novel, large-signal behavioral modeling methodology for Gallium Nitride (GaN) high-electron-mobility transistors (HEMTs), based on the canonical piecewise-linear (CPL) functions, is presented in this paper. The proposed new model employs the poly-harmonic distortion (PHD) model framework, making use of the CPL functions for interpolation of the amplitude of the dominant input signal. The CPL method is also applied to the quadratic PHD (QPHD) model framework, allowing for application to devices operating under high (greater than 3 dB) compression levels. Compared with the standard PHD/QPHD models, which require lengthy tables of parameter values to account for the varying large signal input power(s), the models described in this paper are able to predict transistor behavior at different levels of input power, from the linear region to the strongly nonlinear region (where gain compression exceeds 1 dB), with one single set of model coefficients. The basic theory of the proposed model for both RF and dc responses is provided in the paper. The proposed modeling technique is validated through simulated and experimental data from separate 6 W and 10 W GaN HEMT devices, over a wide range of load conditions and power levels. In addition, a two-dimensional polynomial-based model is used for performance comparison, with the proposed method providing comparable accuracy while requiring significantly fewer model coefficients.
Highlights
Due to the higher power density of Gallium Nitride (GaN) devices when compared with traditional compound semiconductors, such as gallium arsenide (GaAs) [1], there are many recent reports of GaN-based circuits and systems showing excellent performance [2]–[4]
In [19]–[21], more advanced machine learning (ML) techniques are applied to device modeling, using Bayesian inference [19], [20] and support vector regression [21] in order to capture as wide a modeling space as possible i.e. to ensure the models remain accurate across a wide range of power levels and/or frequencies, using a minimal number of measurements
The results show that the proposed canonical piecewise-linear (CPL)-quadratic PHD (QPHD) model can cover a wide range of input power levels, across the full Smith chart, with high prediction accuracy, with 48 model parameters
Summary
Due to the higher power density of GaN devices when compared with traditional compound semiconductors, such as gallium arsenide (GaAs) [1], there are many recent reports of GaN-based circuits and systems showing excellent performance [2]–[4]. In [19]–[21], more advanced machine learning (ML) techniques are applied to device modeling, using Bayesian inference [19], [20] and support vector regression [21] in order to capture as wide a modeling space as possible i.e. to ensure the models remain accurate across a wide range of power levels and/or frequencies, using a minimal number of measurements This approach has led to excellent results. This paper combines the CPL technique with the PHD model formalism in order to eliminate the model dependence on the dominant large-signal tones, resulting in a novel model implementation This new model can capture the device RF/dc behavior across a range of power levels very accurately, using a single set of model parameters. The QPHD model can be expected to give superior results, and in particular to give good prediction for a wider range of reflection coefficients presented to the ports
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