Uncovering spatio-temporal patterns in semiconductor superlattices by efficient data processing tools
Abstract
Time periodic patterns in a semiconductor superlattice, relevant to microwave generation, are obtained upon numerical integration of a known set of drift-diffusion equations. The associated spatio-temporal transport mechanisms are uncovered by applying (to the computed data) two recent data processing tools, known as the higher order dynamic mode decomposition and the spatio-temporal Koopman decomposition. Outcomes include a clear identification of the asymptotic self-sustained oscillations of the current density (isolated from the transient dynamics) and an accurate description of the electric field traveling pulse in terms of its dispersion diagram. In addition, a preliminary version of a novel data-driven reduced order model is constructed, which allows for extremely fast online simulations of the system response over a range of different configurations.
Cite
@article{arxiv.2109.14660,
title = {Uncovering spatio-temporal patterns in semiconductor superlattices by efficient data processing tools},
author = {F. Terragni and L. L. Bonilla and J. M. Vega},
journal= {arXiv preprint arXiv:2109.14660},
year = {2021}
}
Comments
42 pages, 21 figures, preprint version