A number of machine learning (ML) algorithm based small signal modeling of Gallium
Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature …

This article presents accurate, efficient and reliable small-signal model parameter extraction
approaches applied to Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) …

A variety of novel behavioral modeling techniques have been reported to accurately capture
the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large …

In this article, a general theory and associated design methodology for outphasing power
amplifier (OPA) is proposed. The design theory leads to the conclusion that, compared to …

Artificial Neural Network (ANN) is frequently utilized for the development of behavioral
models of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs). However …

The article proposes a modified series compensation (MSC) outphasing power amplifier
(OPA) that maximizes saturation output power and extends power back-off range. The …

Gallium nitride (GaN) high electron mobility transistors (HEMTs) are quintessential prospect
for the design of state-of-the-art power amplifiers (PAs). Within this context, this paper …

This article presents a real-time nonlinear vector network analyzer (NVNA) testbed that
enables the acquisition of the isothermal transient response of GaN HEMTs operating in …

In this paper, a novel technology named Pelican-Gaussian process regression machine
learning algorithm is proposed for modelling the large-signal characteristics of Gallium …

This paper develops and assesses multiple optimized Machine Learning (ML) based
techniques to discover the appropriateness of them in modelling small-signal behaviors of …