The structure of heavily doped impurity band in crystalline host

TL;DR

This paper investigates the complex structure of impurity bands in heavily-doped semiconductors using numerical algorithms and deep learning, revealing multiple mobility edges and phase distinctions within the impurity band.

Contribution

It introduces a combined computational and machine learning approach to analyze impurity band structure, identifying multiple mobility edges in heavily-doped semiconductors.

Findings

Identification of three mobility edges in the impurity band.

Deep learning model accurately distinguishes extended and localized phases.

Rich internal structure of impurity band revealed through IPR and Thouless number calculations.

Abstract

We study the properties of the impurity band in heavily-doped non-magnetic semiconductors using the Jacobi-Davidson algorithm and the supervised deep learning method. The disorder averaged inverse participation ratio (IPR) and thouless number calculation show us the rich structure inside the impurity band. A Convolutional Neural Network(CNN) model, which is trained to distinguish the extended/localized phase of the Anderson model with high accuracy, shows us the results in good agreement with the conventional approach. Together, we find that there are three mobility edges in the impurity band for a specific on-site impurity potential, which means the presence of the extended states while filling the impurity band.