The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (abc), variantabc and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor fiel...
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The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (abc), variantabc and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that abcalgorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic abcalgorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variantabc and basic abcalgorithm for the parameter extraction of the MOSFET model;also the implementation of the abcalgorithm is shown to be simpler than that of the PSO algorithm.
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