Investigation of electron mobility in GaAs‐based devices using genetic algorithm

Abstract

PurposeThe purpose of this work is to study the capability of heuristic algorithms like genetic algorithm to estimate the electron transport parameters of the Gallium Arsenide (GaAs). Also, the paper provides a simple but complete electron mobility model for the GaAs based on the genetic algorithm that can be suitable for use in simulation, optimization and design of GaAs‐based electronic and optoelectronic devices.Design/methodology/approachThe genetic algorithm as a powerful heuristic optimization technique is used to approximate the electron transport parameters during the model development.FindingsThe capability of the model to approximate the electron transport properties of Gallium Arsenide is tested using experimental and Monte Carlo data. Results show that the genetic algorithm based model can provide a reliable estimate of the electron mobility in Gallium Arsenide for a wide range of temperatures, concentrations and electric fields. Based on the obtained results, this paper shows that the genetic algorithm can be a useful tool for the estimation of the transport parameters of semiconductors.Originality/valueFor the first time, the genetic algorithm is used to calculate the electron transport parameters in Gallium Arsenide. A complete electron mobility model for a wide range of temperatures, doping concentrations, compensation ratios and electric fields is developed.