Berlin 2018 – scientific programme
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O: Fachverband Oberflächenphysik
O 97: Scanning probe techniques: Method development II
O 97.2: Talk
Thursday, March 15, 2018, 15:15–15:30, MA 005
Two-degree-of-freedom control combining machine learning and extremum seeking for fast scanning quantum dot microscopy — •Michael Maiworm1, Christian Wagner2,3, Ruslan Temirov2,3, F. Stefan Tautz2,3, and Rolf Findeisen1 — 1Institute for Automation Engineering (IFAT), Otto-von-Guericke Universität Magdeburg, Germany — 2Peter Grünberg Institut (PGI-3), Forschungszentrum Jülich, Germany — 3JARA-Fundamentals of Future Information Technology, Jülich, Germany
Scanning Quantum Dot Microscopy (SQDM) is a novel technique for the quantitative imaging of surface potential distributions with nanometer resolution[1,2]. The SQDM sensor is a molecular quantum dot (QD) attached to the tip of a non-contact AFM by molecular manipulation. Surface potential variations can change the QD charge state via gating which causes sharp dips in NC-AFM Δ f(Vbias) spectra. Mapping of the surface potential is thus possible via mapping of the Vbias values at which the respective dips appear. For this purpose we present a two-degree-of-freedom control approach, consisting of an extremum seeking controller and a feedforward control. The feedforward signal is based on a machine learning approach where a Gaussian process is used to capture the already scanned part of the image and compute a prediction for the next scan lines. The proposed control approach speeds up the scanning process by one order of magnitude and enables to scan large areas and strong potential variations. [1] C. Wagner et al. Phys. Rev. Lett. 115, 026101 (2015) [2] M. Green et al. Japan. J. Appl. Phys. 55, 08NA04-7 (2016)