Establishing composition dependent k · p parameters for (Al,Ga)N alloys
Loading...
Date
2024-10-23
Authors
Singh, Amit Kumar
Gomez-Iglesias, Alvaro
Schulz, Stefan
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Deep UV (Al,Ga)N-based light emitters exhibit very low quantum efficiencies when compared to UV emitters at longer wavelengths. To improve the efficiencies of e.g. (Al,Ga)N-based light emitting diodes, theory and simulation can help to guide the device design. The theoretical framework underlying device simulations is often based on drift-diffusion models coupled with a self-consistent Schrodinger-Poisson equation solver. To achieve accurate and predictive models, understanding the composition dependence of material input parameters is of central importance. We target the composition dependence of k⋅p parameters in AlxGa(1−x)N alloys by using density functional theory (DFT) to obtain effective band structures from alloy disordered supercells. Building on these effective band structures, a numerically efficient fitting scheme based on the Sobol-sequence method is employed to extract effective electron masses, me(x) , Luttinger-like parameters Ai(x) , with i=1…6 , and crystal field splitting energy, ΔCF , as a function of Al content, x , in the system. Our calculations reveal that for me(x) a linear interpolation of the GaN and AlN values provides a good description of the DFT data. However, for Ai(x) and ΔCF(x) this simple approximation breaks down and we find that composition dependent bowing parameters are required to describe effective DFT band structures accurately.
Description
Keywords
(AlGa)N , DFT , k · p , Alloys , Electronic structure
Citation
Singh, A. K., Gomez-Iglesias, A. and Schulz, S. (2024) 'Establishing composition dependent k · p parameters for (Al,Ga)N alloys', 2024 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), New Delhi, India, 23-27 September 2024, pp. 95-96. https://doi.org/10.1109/NUSOD62083.2024.10723647
Link to publisher’s version
Copyright
© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.