Machine Learning Methods for Background Potential Estimation in 2DEGs
Carlo da Cunha Nobuyuki Aoki David Ferry Kevin Vora Yu Zhang
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.