Accurate, Efficient and Reliable Small-Signal Modeling Approaches for GaN HEMTs

Abstract

This article presents accurate, efficient and reliable small-signal model parameter extraction approaches applied to Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). Firstly, a scanning-based systematic model parameter extraction methodology is developed. Then, newly reported Optimization Algorithms (OAs) namely Marine Predators Algorithm (MPA), Pelican Optimization Algorithm (POA) and Tunicate Swarm Algorithm (TSA) in combination with direct extraction method are utilized to develop hybrid model parameter extraction methodologies. Lastly, both the scanning-based systematic and OA-based hybrid modelling procedures are thoroughly validated and demonstrated on a GaN HEMT grown on diamond substrate to identify their pros and cons in distinct application settings. Moreover, reliability, accuracy, convergence behavior, complexity and execution time of MPA-, POA- and TSA-based hybrid extraction procedures are also discussed. We found that both classes of the approaches are able to produce an excellent agreement between the measured and modelled S-parameters for a wide frequency range up to 40 GHz. However, OA-based hybrid modelling procedures are more physically relevant.

Keywords

• GaNHEMT
• marine predators algorithm (MPA)
• parameter extraction
• pelican optimization algorithm (POA)
• small-signal models (SSMs)
• tunicate swarm algorithm (TSA)