Gallium Nitride Power Electronics using Machine Learning
type: CreativeWork
description: Gallium Nitride (GaN) power devices have the potential to jump-start the next generation of power converters which are smaller, faster, denser, and cheaper. They are thus expected to meet the increasing 21st Century need for power density and efficiency, while at the same time reducing pollution. With the commercialisation of 600 V GaN power devices, which the industry is keen to adopt, come significant challenges. Since there are a number of such devices which are new to the power community, there is a steep learning curve involved, with dispersed information on how best to employ these devices. This work aims to solve this problem through the development of a universal GaN power device and circuit model and the formulation of design rules and guidelines. Through this contribution, designers will be able to better understand and work with these novel devices with relative ease. This will aid the need for faster adoption of GaN devices by the industry solving the barriers to commercialisation. This research demonstrates the use of machine learning (ML) algorithms for behavioural modelling of GaN power devices. Introducing ML as the key to developing a general behavioural and circuit model for GaN power devices combined with understanding, learning, customizing and successfully demonstrating it is the major contribution of this research work. This research first presents a comprehensive investigation into the parasitic effect on the GaN device switching performance. A simple process based on RF techniques is introduced to approximately extract the impedances of the GaN device to develop a behavioural model. The switching behaviour of the model is validated using simulation and double pulse test experiments at 450 V, 10 A test conditions. The developed behavioural model for Transhporm GaN HEMT is 95.2% accurate as the existing LT-spice manufacturer model, and is very much easier for power designers to handle, without the need for knowledge about the physics or geometr…
abstract: Gallium Nitride (GaN) power devices have the potential to jump-start the next generation of power converters which are smaller, faster, denser, and cheaper. They are thus expected to meet the increasing 21st Century need for power density and efficiency, while at the same time reducing pollution. With the commercialisation of 600 V GaN power devices, which the industry is keen to adopt, come significant challenges. Since there are a number of such devices which are new to the power community, there is a steep learning curve involved, with dispersed information on how best to employ these devices. This work aims to solve this problem through the development of a universal GaN power device and circuit model and the formulation of design rules and guidelines. Through this contribution, designers will be able to better understand and work with these novel devices with relative ease. This will aid the need for faster adoption of GaN devices by the industry solving the barriers to commercialisation. This research demonstrates the use of machine learning (ML) algorithms for behavioural modelling of GaN power devices. Introducing ML as the key to developing a general behavioural and circuit model for GaN power devices combined with understanding, learning, customizing and successfully demonstrating it is the major contribution of this research work. This research first presents a comprehensive investigation into the parasitic effect on the GaN device switching performance. A simple process based on RF techniques is introduced to approximately extract the impedances of the GaN device to develop a behavioural model. The switching behaviour of the model is validated using simulation and double pulse test experiments at 450 V, 10 A test conditions. The developed behavioural model for Transhporm GaN HEMT is 95.2% accurate as the existing LT-spice manufacturer model, and is very much easier for power designers to handle, without the need for knowledge about the physics or geometr…
author: Hari, Nikita
language: en
keywords: Gallium Nitride, Machine Learning, Power Electronics, Modelling, Switching
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/83899215-e355-460b-a91c-5baf47acfc9f/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2019-05-18
date modified: 2026-07-22T22:24:21Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.35893