Uncovering spatio-temporal patterns in semiconductor superlattices by efficient data processing tools

TL;DR

This paper applies advanced data processing techniques to analyze spatio-temporal patterns in semiconductor superlattices, revealing underlying mechanisms and enabling fast simulations of their dynamic behavior.

Contribution

It introduces the use of higher order dynamic mode decomposition and Koopman decomposition to uncover transport mechanisms and develops a preliminary data-driven reduced order model for rapid system simulations.

Findings

Identification of asymptotic self-sustained oscillations

Description of electric field traveling pulses via dispersion diagram

Development of a fast online simulation model

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.