DEVELOPMENT AND OPTIMIZATION OF ML BASED COMPREHENSIVE MODELLING FRAMEWORK FOR GAN HEMTS
Loading...
Date
2024-04-24
Authors
Saddam Husain
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Radio Frequency (RF) Power Amplifier (PA) is one of the most pivotal constituents
of any wireless transceivers. However, continual advancements and ever-increasing
complexity in the wireless communication technologies demand frequent innovations
in the design of RFPAs. The quality of the designed RFPAs are generally evaluated
based around two basic figures of merits namely efficiency and linearity. Thus, the
RFPAs should provide maximum power and efficiency while maintaining highly linear
operation. In literature, two primary PA design mechanisms, namely measurement- and
modeling-based techniques have been extensively utilized. Each class of technique
has pronounced merits, limitations and applications. However, owing to the seamless
integration ability of the modeling-based techniques with Computer-Aided Design
(CAD) tools, they are increasingly becoming more popular.
The design and innovation in RFPAs are excessively contingent on the measurement
facilities and the Large Signal Models (LSMs) of transistor devices. At present,
Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) technology is
regarded as an optimal microwave transistor technology for the design of RFPAs in
advanced RF/microwave and high power switching applications. This is due to their
attributes namely high energy bandgap, high saturation velocity, high electron mobility,
exceptional thermal behavior and high breakdown field. Furthermore, GaN HEMTs
manifest high power density, thus a smaller size device can be used to sustain a high
power demand. It also implies reduced lower capacitances and lower combining losses
in the design of RFPAs and Low-Noise Amplifiers (LNAs). At this point, it is essential
to mention that, in general, the available LSMs of GaN HEMTs are very specific and
therefore not readily useful for broad range of PA designs. Therefore, there is a pressing
requirement to develop accurate, reliable, efficient and robust LSMs of GaN HEMTs
which can be readily incorporated in CAD tools. Nevertheless, Small-Signal Model
(SSM) development is the first step in pursuit of developing accurate and efficient LSMs.
But, both SSMs and LSMs of GaN HEMTs are essential for the design of accurate,
efficient and reliable GaN HEMT based RFPAs. Apparently, various modeling schemes
have been exploited to develop SSMs and LSMs for GaN HEMTs, however, usually,
they are classified into three main groups, which are physics-based, Equivalent Circuit
(EC) and Behavioral Modeling (BM) frameworks.
This thesis is originated in response to the scientific and technical challenges in EC
and BM frameworks for GaN HEMTs at high frequency applications. Among these challenges, the major focuses are on the development of SSMs for GaN HEMTs, which
are simple, accurate, computational and time efficient, reliable, scalable, and CAD
adaptable. Furthermore, special attention is given to develop SSMs, which manifest
strong interpolation and extrapolation abilities. The developed SSMs are then utilized
to realize the eventual LSMs for GaN HEMTs. In order to develop SSMs and LSMs
for GaN HEMTs, which possess the above-mentioned characteristics, in this thesis,
Machine Learning (ML) based approaches have been explored and utilized because of
their superior learning, prediction, and extrapolation abilities. However, it is pertinent to
state that the ML based modelling of GaN HEMTs is still in its early exploration phase,
and various issues related to this type of modelling are unexplored and not thoroughly
discussed in literature.
It is therefore, in this thesis, an extensive appraisal and analysis of ML and
optimization based small-signal and large-signal modelling for GaN HEMTs have
been presented. In the first part of this thesis, a detailed comparative analysis of
EC based accurate, robust and efficient SSM parameter extraction methodologies for
GaN-on-Diamond HEMTs has been demonstrated. For this, initially, a Scanning-
Based Systematic (SBS) model parameter extraction approach is developed and
applied on GaN-on-Diamond HEMTs. Thereafter, marine predators algorithm,
pelican optimization algorithm and tunicate swarm algorithm, the recently developed
Optimization Algorithms (OAs), based hybrid extraction methodologies have been
developed and applied on the same GaN HEMTs. Finally, a detailed comparison of
OAs and SBS modelling schemes by using SBS extraction approach as a benchmark in
terms of reliability, accuracy, convergence behavior, complexity, execution time, and
scalability is provided and thoroughly discussed.
Accurate, efficient and CAD compatible small-signal behavioral models for GaN
HEMTs using Artificial Neural Network (ANN), Support Vector Regression (SVR)
and Gaussian Process Regression (GPR) based ML techniques have been developed,
validated and discussed in the subsequent part of this thesis. These ML based approaches
have been applied on many GaN HEMTs devices grown on Silicon (Si), Silicon Carbide
(SiC) and Diamond substrates. Furthermore, a meticulous evaluation of ANN algorithms
implemented in MATLAB, Python (using Keras, PyTorch and Scikit-learn) and R
(using H2O) for small-signal behavioral modelling of GaN HEMTs has been presented.
To establish the appropriateness of software environments in distinct application
settings, the developed models are examined on a range of metrics namely behavior
on the unseen data, training and prediction speed and ADS adaptability, and software
environments are surveyed for support and documentation, user-friendly interface,
simplicity in the model development procedure, open-access and cost. Optimization
of the hyperparameters of ML algorithms is vital to realize the best possible models.
In this context, hybrid optimized ML algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) assisted ANN, PSO and RSO assisted SVR, RSO
assisted GPR, and RSO assisted various tree-based models are explored and developed.
Finally, the developed models are evaluated on many regression tests to identify the
most fitting ML algorithms for particular applications.
Finally, the last part of the thesis presents ML based CAD adaptable advanced
models and applications. Initially, GPR, GA-ANN and RSO-Decision trees based
SSMs for GaN HEMTs are developed. Then, the integration of these developed models
with ADS are presented by inserting the developed ML based models into a design of
class-F PA. Subsequently, to examine the accuracy of the models, stability and gain
tests of the GaN HEMT based amplifier configuration are performed. Thereafter, using
the developed SSMs, a joint EC-behavioral LSM for a GaN HEMT is developed and
presented. The intrinsic drain and gate currents are modelled using GA-ANN, PSOSVR
and GPR based approaches. These current modelling approaches are compared in
terms of simplicity in the model development stage, computational efficiency, accuracy
and required time to simulate the currents. At last, LSM validation and realization using
GA-ANN based approach are demonstrated on a design of an inverse class-F PA.
Description
Keywords
GaN HEMTs, ML, Behavioral modelling, Small-Signal Modelling, Large-Signal Modelling, Joint EC-BM, GaN based class-F PA, Type of access: Restricted
Citation
Husain, S. (2024) Development and Optimization of ML Based Comprehensive Modelling Framework for GaN HEMTs. Nazarbayev University School of Engineering and Digital Sciences.