Fusing Online Gaussian Process-Based Learning and Control for Scanning Quantum Dot Microscopy

Elucidating electrostatic surface potentials contributes to a deeper understanding of the nature of matter and its physicochemical properties, which is the basis for a wide field of applications. Scanning quantum dot microscopy, a recently developed technique allows to measure such potentials with a...

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Bibliographic Details
Published in:arXiv.org 2020-04
Main Authors: Pfefferkorn, Maik, Maiworm, Michael, Wagner, Christian, Tautz, F Stefan, Findeisen, Rolf
Format: Article
Language:English
Subjects:
Approximation
Control theory
Feedforward control
Gaussian process
Mathematical analysis
Microscopy
Optimization
Quantum dots
Training
Online Access:Get full text
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Summary:Elucidating electrostatic surface potentials contributes to a deeper understanding of the nature of matter and its physicochemical properties, which is the basis for a wide field of applications. Scanning quantum dot microscopy, a recently developed technique allows to measure such potentials with atomic resolution. For an efficient deployment in scientific practice, however, it is essential to speed up the scanning process. To this end we employ a two-degree-of-freedom control paradigm, in which a Gaussian process is used as the feedforward part. We present a tailored online learning scheme of the Gaussian process, adapted to scanning quantum dot microscopy, that includes hyperparameter optimization during operation to enable fast and precise scanning of arbitrary surface structures. For the potential application in practice, the accompanying computational cost is reduced evaluating different sparse approximation approaches. The fully independent training conditional approximation, used on a reduced set of active training data, is found to be the most promising approach.
ISSN:2331-8422