Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy
arXiv:2308.13372 · doi:10.21468/SciPostPhys.15.6.242
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.
arXiv comment23 pages, 12 figures, Submission to SciPost
References in corpus (14)
- Kwant: a software package for quantum transport
- Fractional statistics in anyon collisions
- Coherent Branched Flow in a Two-Dimensional Electron Gas
- On the imaging of electron transport in semiconductor quantum structures by scanning-gate microscopy: successes and limitations
- Sound-driven single-electron transfer in a circuit of coupled quantum rails
- Electron interferometer formed with a scanning probe tip and quantum point contact
- Local densities, distribution functions, and wave function correlations for spatially resolved shot noise at nanocontacts
- Scanning gate experiments: from strongly to weakly invasive probes
- Partial local density of states from scanning gate microscopy
- The self-consistent quantum-electrostatic problem in strongly non-linear regime
- Unveiling the charge distribution of a GaAs-based nanoelectronic device: A large experimental data-set approach
- Energy Stability of Branching in the Scanning Gate Response of Two-Dimensional Electron Gases with Smooth Disorder
- Deep neural networks for inverse problems in mesoscopic physics: Characterization of the disorder configuration from quantum transport properties
- Accurate characterization of tip-induced potential using electron interferometry
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