Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy

Abstract

The weak disorder potential seen by the electrons of a two-dimensional electron gas in high-mobility semiconductor heterostructures leads to fluctuations in the physical properties and can be an issue for nanodevices. In this paper, we show that a scanning gate microscopy (SGM) image contains information about the disorder potential, and that a machine learning approach based on SGM data can be used to determine the disorder. We reconstruct the electric potential of a sample from its experimental SGM data and validate the result through an estimate of its accuracy.

23 pages, 12 figures, Submission to SciPost

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Cited by in corpus (5)

• Artificial Intelligence for Quantum Computing
• Machine Learning the Disorder Landscape of Majorana Nanowires
• Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires
• Supercurrent modulation in InSb nanoflag-based Josephson junctions by scanning gate microscopy
• Electrostatics in semiconducting devices I : The Pure Electrostatics Self Consistent Approximation