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

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arxiv 2308.13372 v2 pith:6OSG276V submitted 2023-08-25 cond-mat.mes-hall cond-mat.dis-nn

Gaëtan J. Percebois , Antonio Lacerda-Santos , Boris Brun , Benoit Hackens , Xavier Waintal , Dietmar Weinmann This is my paper · ORCID
classification cond-mat.mes-hallcond-mat.dis-nn
keywords potentialdisorderdatagatehigh-mobilitymicroscopyscanningsemiconductor
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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    A vision transformer predicts topological visibility and LDOS-based Majorana indicators from simulated nanowire conductance traces with high held-out accuracy, but with no experimental validation.

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