Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/230691
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dc.titleADVANCED MODELLING OF GaAs HBTs AND GaN HEMTs FOR RF APPLICATIONS
dc.contributor.authorHU WENRUI
dc.date.accessioned2022-08-31T18:00:45Z
dc.date.available2022-08-31T18:00:45Z
dc.date.issued2022-04-19
dc.identifier.citationHU WENRUI (2022-04-19). ADVANCED MODELLING OF GaAs HBTs AND GaN HEMTs FOR RF APPLICATIONS. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/230691
dc.description.abstractAccurate models for RF/microwave transistors are essential for circuit design using electronic design automation tools. Many empirical and artificial neural network (ANN)-based models have been developed. However, empirical models are becoming increasingly complex to capture thermal effects. ANN-based modelling is sensitive to outliers, and some ANN-based models are not charge-conservative. In addition, ANN architecture is determined by a trial-and-error process. This thesis presents the small- and large-signal modelling of gallium arsenide (GaAs) heterojunction bipolar transistors (HBTs) and gallium nitride (GaN) high electron mobility transistors (HEMTs). Measurement data are obtained for model development and verification. Firstly, a dimension reduction method is investigated for fast temperature-dependent current modelling. Secondly, hybrid extraction and outlier detection are developed to obtain reliable datasets for accurate ANN-based modelling. Furthermore, an ANN-based consistent gate charge model is proposed. Lastly, an evolutionary multilayer perceptron-based modelling approach is proposed to construct an accurate model with a proper architecture.
dc.language.isoen
dc.subjectArtificial neural network, large-signal modeling, self-heating effects, small-signal parameter extraction, trapping effects
dc.typeThesis
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.supervisorYongxin Guo
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY (CDE-ENG)
dc.identifier.orcid0000-0001-5514-4130
Appears in Collections:Ph.D Theses (Open)

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