Hybrid Neural Network Modeling of Anion Exchange at the Interfaces of Mixed Anion III–V Heterostructures Grown by Molecular Beam Epitaxy

Abstract

A hybrid neural network model is constructed by characterizing the growth of GaAs/sub 1-y/P/sub y/-GaAs superlattices (SLs) grown on [001] GaAs substrates by molecular beam epitaxy. These heterostructures are formed by the P/sub 2/ exposure of an As-stabilized GaAs surface, and ex situ high-resolution X-ray diffraction (HRXRD) is performed to determine the phosphorus composition at the interfaces. A first-order kinetic model is then developed to describe the mechanisms of anion exchange, surface desorption, and diffusion. A semi-empirical hybrid neural network is used to estimate the parameters of the kinetic model and analyze the microscopic processes occurring at the interfaces of the mixed anion III-V heterostructures. The phosphorus diffusion process in GaAs is estimated to have a diffusion coefficient of D=1.4/spl times/10/sup -14/exp(-0.11 eV/k/sub B/T/sub s/) cm/sup 2//spl middot/s/sup -1/ for samples with P/sub As4/=4/spl times/10/sup -6/ torr and exhibits enhanced phosphorus intermixing for samples with lower As-stabilizing fluxes.