Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis

Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis

Precise modeling of gallium nitride (GaN) high-electron mobility transistors (HEMTs) is vital for ensuring reliable and scalable RF circuit design, and efficient characterization of the device behavior. This article presents robust hybrid equivalent circuit (EC)–machine learning (ML) fram...

Full description

Saved in:
Bibliographic Details
Main Authors: Ahmad Khusro, Saddam Husain, Mohammad Hashmi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
AlGaN/GaN HEMT
data-driven design
hybrid modeling
intrinsic parameter extraction
machine learning
RF transistor modeling
Online Access:https://ieeexplore.ieee.org/document/11105079/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/11105079/

Similar Items