A Python code for simulations of RHEED intensity oscillations within the one-dimensional dynamical approximation

Abstract

We present a Python-based implementation of a practical procedure of construction of simulation program, which facilitates the calculation of changes to the intensity of RHEED oscillations in the function of the glancing angle of incidence of the electron beam, employing various models of crystal potential for heteroepitaxial structures including the possible existence of various diffuse scattering models through the layer parallel to the surface. The calculations are based on the use of a one-dimensional dynamical diffraction theory. Although this theory has some limitations, in practice it is useful under so-called one-beam condition. Computation performance has been improved by using Numba as an open source, NumPy-aware optimising compiler for Python.

Program Summary

Program Title: PY_RHEED_DIFF

CPC Library link to program files: https://doi.org/10.17632/j6jxt9yr3b.1

Licensing provisions: GNU General Public License 3

Programming language: Python 3.12.7

Journal reference of previous version: Computer Physics Communications 185 (2014) 3001–3009

Does the new version supersede the previous version?: Yes.

Reasons for the new version: Python, as a powerful, accessible and general-purpose programming language, has gained tremendous popularity in recent years. Python is characterised by a remarkable simplicity that makes it an ideal choice for users for whom knowledge of high-level programming techniques is not the most important in research work. According to users’ suggestions we have developed a Python-based implementation of generic computational model for simulations of changes to the intensity of RHEED oscillations in the function of the glancing angle of incidence of the electron beam, employing various models of crystal potential for heteroepitaxial structures including the possible existence of various diffuse scattering models through the layer parallel to the surface. This version implements improvements for ergonomics, computational performances, readability, and code functionality by adding new capabilities which make the output data generation and visualisation process much more efficient compared to the previous version.