The compact model plays a pivotal role as a critical link between device fabrication and
circuit design. While conventional compact model theories and techniques are generally …

In this article, an artificial neural network (ANN)-based large-signal model (LSM) of
AlGaN/GaN high electron mobility transistors (HEMTs) with accurate buffer-related trapping …

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 …

The work reported in this article explores a novel Particle Swarm Optimization (PSO) tuned
Support Vector Regression (SVR) based technique to develop the small-signal behavioral …

Charge trapping effects represent a major challenge in the performance evaluation and the
measurement-based compact modeling of modern short-gate-length (ie,≤ 0.15 μm) Gallium …

Neural networks are widely used to build large-signal models; an appropriate network
architecture is important for high model accuracy and good generalization ability. In this …

This article deals with the characterization of charge trapping dynamics in a novel 100-nm
double-heterojunction AlN/GaN/AlGaN-on-Si radio frequency (RF) HEMT process. In order …

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 …

A new method is proposed for the accurate experimental characterization and fully
automated extraction of compact nonlinear models for field-effect transistors (FETs). The …