Abstract
A computational diffusion model is used to predict the lateral thickness and composition profiles of InxGa1−xP stripes grown by selective-area, atmospheric pressure metalorganic chemical vapor deposition. Standard profilometry is used to measure the thickness profiles of InP and GaAs stripes grown on SiO2 patterned InP and GaAs substrates, respectively. The model is used to find self-consistent empirical diffusion parameters for the In and Ga components which yield fits to the measured thickness data. The InP and GaAs data is then used to predict the growth thickness profile of InGaP by a weighted sum of the predicted profiles of the InP and GaP binary constituents. InGaP composition profiles are calculated by taking the ratio of the InP deposited volume to the InGaP deposited volume predicted by the model at each of the simulation points. Predicted thickness profiles are verified by standard profilometry, and composition profiles are verified by secondary ion mass spectrometry imaging using a fast resistive anode encoding detector. It is found that the measured thickness and composition profiles agree well with the profiles predicted by the model, thus verifying that the model can be used for the InGaP material system. The derived empirical parameters are used to predict the thicknesses and compositions of selectively grown InGaP quantum wells as a function of oxide width.
Published Version
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