Machine Learning Methods for Background Potential Estimation in 2DEGs

TL;DR

This paper explores machine learning techniques, including GANs, cellular neural networks, and evolutionary algorithms, to estimate background potentials in 2DEGs from SGM data, addressing impurity effects in quantum materials.

Contribution

It introduces a novel application of machine learning methods, especially evolutionary search, for analyzing 2DEG background potentials from SGM data, overcoming data limitations.

Findings

Evolutionary search outperforms other methods in potential estimation.

Machine learning enhances defect analysis in 2DEGs.

The approach aids understanding of impurity effects in quantum devices.

Abstract

In the realm of quantum-effect devices and materials, two-dimensional electron gases (2DEGs) stand as fundamental structures that promise transformative technologies. However, the presence of impurities and defects in 2DEGs poses substantial challenges, impacting carrier mobility, conductivity, and quantum coherence time. To address this, we harness the power of scanning gate microscopy (SGM) and employ three distinct machine learning techniques to estimate the background potential of 2DEGs from SGM data: image-to-image translation using generative adversarial neural networks, cellular neural network, and evolutionary search. Our findings, despite data constraints, highlight the effectiveness of an evolutionary search algorithm in this context, offering a novel approach for defect analysis. This work not only advances our understanding of 2DEGs but also underscores the potential of machine learning in probing quantum materials, with implications for quantum computing and nanoelectronics.