Work

Machine-learning-assisted optimization of Ga-free type-II superlattices for enhanced vertical hole mobility

Year: 2024

Type: article

Abstract: Gaussian process regression is used to develop a model for predicting carrier transport in superlattice (SL) structures grown on GaSb and 6.2 Å substrates. This model is used to search SL structures optimized for enhanced hole transport in the vertical (growth) direction. Nonequilibrium Green’s functions calculations are used to determine the vertical hole mobility of several chosen structures in both ideal and disordered cases. It is demonstrated that the conductivity effective mass can be used in some cases as a qualitative predictor for the relative hole mobility between different structures. However, in the case of disordered SLs, the effective mass must be calculated from quasi-random disordered structures as the results may differ significantly from the ideal case. Ultimately, a methodology for predicting SL structures optimized for high hole transport efficiency in the case of ideal and disordered SLs is demonstrated.

Source: Journal of Applied Physics

DOI: 10.1063/5.0218238

Authors: John Glennon, Enrico Bellotti

Institution: Boston University

Language: English

Topic: Semiconductor Quantum Structures and Devices

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funders: Politecnico di Torino, Army Research Laboratory, DEVCOM Army Research Laboratory