Switch modeling and drive control of GaN HEMTs: current status, challenges and prospects
Source abstract
Abstract
Purpose Gallium nitride high electron mobility transistors (GaN HEMTs) are representative wide-bandgap semiconductor devices characterized by their superior high-frequency switching characteristics. This paper aims to review their modeling methods and drive control technologies, systematically summarize the research progress and predict the future development trends. Design/methodology/approach This paper first introduces the background and advantages of GaN HEMTs and then reviews four modeling methods: physical model, behavioral model, analytical model and hybrid model. It also summarizes their respective characteristics and application scopes. Furthermore, key drive control techniques are discussed in depth. Finally, the development trend of modeling technology is predicted by combining the characteristics of typical devices. Findings Switching modeling methods for GaN HEMTs have advanced to better address high-frequency and high-power application requirements. Each model type offers distinct advantages regarding accuracy, complexity and physical interpretability. Integrating appropriate drive control strategies further enhances system performance. Originality/value This paper summarizes four switching models for GaN HEMTs and compares their characteristics and applicable scenarios. Subsequently, it further explores the driving characteristics and key technologies of GaN HEMTs, including dead time control, segmented driving and level shifting techniques. Finally, future development trends are discussed in light of practical device effects, with a particular focus on multiphysics coupling, emerging artificial intelligence (AI) modeling and adaptive driving technologies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: GaN-based semiconductor devices and materials · Silicon Carbide Semiconductor Technologies · Advanced Power Amplifier Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guangdong University of Technology School of Automation, , Guangzhou,
FernUniversität in Hagen Faculty of Mathematics and Computer Science, , Hagen,
References (100+)
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