Abstract

Abstract

Abstract
This paper presents an efficient parameter extraction method applied to GaN high electron mobility transistors (HEMTs) for mm-wave applications. The procedure is based on S-parameters measurements at cold bias condition to extract the extrinsic parameters of a 19-element small-signal model. Hybrid technique of particle-swarm-optimization and direct fitting has been developed and implemented. The extraction procedure has been optimized to consider measurements uncertainty and improve the reliability of the extraction. The model has been validated by S-parameters measurements at different bias conditions and wide frequency range. A very good agreement between simulations and measurements has been obtained.

Abstract
An improved and efficient parameter extraction method applied to GaN high electron mobility transistors (HEMTs) is presented. The aim is to extract reliable values for the generally distributed small-signal model to accurately describe the device at mm-wave range. A modified version of hybrid extraction technique combining particle-swarm-optimization and direct fitting has been implemented. S-parameters fitting has been used to validate the model under various bias conditions. The results indicate a very good agreement between model and simulation up to 60 GHz.

Abstract
This paper presents a comparison between different optimization techniques in the context of hybrid small-signal model (SSM) parameter extraction for GaN High Electron Mobility Transistors (HEMTs). The optimization techniques considered in this work are: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The algorithms were tested for their robustness, convergence speed, and efficiency. PSO algorithm was shown to be the most suitable in terms of robustness and efficiency. However, all techniques were able to obtain credible model parameters, which confirms the reliability of the adopted procedure. The quality of extraction was evaluated by means of S-parameter fitting at different bias conditions.

Abstract