Gallium Nitride Power Electronics using Machine Learning
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 geometry of the device. However, given that separate models would need to be developed for each commercial GaN device, the need for a generalized and accurate GaN behavioural model was identified, and it is this generalised model that the remainder of this thesis focuses on.
In the next part of this research, a GaN platform test bench is built through bridging RF and power electronics design methodologies to achieve a gate loop and power loop inductance of around 1.8nH with switching waveforms with rise time and fall time around 2.5ns at 450V, 15A, 500KHz test conditions. The double pulse test circuits are customized using different off the shelf gate drives and analysed for collecting switching data for training the ML model.
ML modelling using supervised learning is used to predict the switching voltage and current waveforms thus making it possible to construct a generic GaN black box model. Different architectures with single and multi- layer neural networks are explored for modelling. The ability to demonstrate a GaN device ML model that maps both voltage and current inputs and outputs is another characteristic and novel feature of this work.
This research demonstrates different types of GaN ML models. The developed voltage and current prediction models are based on feed forward neural network (FFNN), long short-term memory unit (LSTM) and gated recurrent unit (GRU). Several parameters are quantified and compared for validating the models. They are the network architectures, parameters, training time, validation loss and error loss. The ML models are also compared with the demonstrated model of chapter 3 and existing LT-Spice manufacturer models. The results show that the author has been able to develop a GaN LSTM ML model with an error rate of 0.03, and convergence at 3s with excellent stability.
The ML based modelling is then translated from GaN power devices to GaN based circuits. Among the different neural network architectures trained and tested, a multi FFNN with 5 hidden layers and 30 neurons, was found to be the best for prediction and optimization. The switching behaviour comparison results shows the benefits and value of ML modelling in opening up whole new possibilities of employing advanced control algorithms for very efficient, reliable and scalable performance of GaN power electronics systems.
Finally, the findings of this work have been generalized to frame machine learning based techniques to address the need for generic modelling of power electronic devices. These solutions are presented as an information manual to researchers, engineers and students interested in benefiting from adopting machine learning for power electronics applications.