Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session J10: Advances in Scanned Probe Microscopy 4: Machine Learning for Correlative and Analytical Measurements in Scanning Probe MicroscopyFocus
|
Hide Abstracts |
Sponsoring Units: GIMS Chair: Neus Domingo, Institut Català de Nanociència i Nanotecnologia Room: 108 |
Tuesday, March 3, 2020 2:30PM - 2:42PM |
J10.00001: Surface Structure of α-Quartz(0001) Kristen Burson, Georg H. Simon, Clare M. Munroe, Markus Heyde, H J Freund Crystalline silica (SiO2) surfaces play an important role in nature and technology, and are often used as substrates in scientific studies. Despite its importance, the atomic surface structure of quartz still remains poorly understood, especially on the experimental side. Here we present a real space structure study of α-quartz(0001) surfaces prepared at high temperature in air. The preparation approach is highly reproducible and leads to well-defined, flat, and clean terraces in ambient conditions. Using high-resolution atomic force microscopy (AFM), we observed a large scale surface termination with a reconstruction periodicity of 5 nm, consistent with previous literature [1,2]. By characterizing the samples with AFM in humid air, dry nitrogen, and water we establish insight into surface morphology and reconstruction. Using the enhanced resolution capabilities of atomic force microscopy in liquid water, we address the real space atomic scale structure of this surface and find that the lattice dimensions agree with the bulk crystal lattice constants for α-quartz. |
Tuesday, March 3, 2020 2:42PM - 2:54PM |
J10.00002: Nonlinearity-induced frequency mixing in AFM: novel contrast imaging with machine learning Greg Haugstad, Andrew Avery, Stephan Hubig, Rachel Rahn, Alon McCormick, Bing Luo, Han Seung Lee On complex thin films pertinent to lubrication and superhydrophilicity, dynamic AFM methods were used to sense both the elastic and viscous response to an AFM tip. A novel strategy in "multifrequency" AFM -- vibrating the AFM microcantilever (to which the tip is attached) at two tones near the fundamental flexural resonance -- generates dozens of intermodulation tones of response due to nonlinear tip-sample interaction (a well-known concept in electrical engineering usually in the context of AC signal distortion). This method1 both expands contrast mechanisms -- with images of amplitude and phase at each mixing tone via 40 FPGA lock-in amplifiers -- and the ability to reconstruct the distance dependence of conservative and dissipative response at each image pixel via a 40-term discrete Fourier transform of tip motion. This reconstruction is an unprecedented capability in "force spectroscopy" (probing distance dependent interactions). Machine learning, to cluster force-spectroscopic fingerprints and thereby generate higher signal-to-noise images, is further discussed. |
Tuesday, March 3, 2020 2:54PM - 3:06PM |
J10.00003: Light Scattering and Localization by Dislocation Scattering Sites Farbod Shafiei, Tommaso Orzali, Alexey Vert, Mohammad-Ali Miri, PY Hung, Man Hoi Wong, Andrea Alu, Gennadi Bersuker, Michael C Downer Lattice mismatch between epitaxialy grown crystalline thin film over substrate introduce variety of subsurface defects including threading dislocations. Dislocations defects behave as an acceptor traps for electron and thus acting as Coulomb scattering sites. Multi-scattering and localization of the light from these atomic defects scattering sites was collected by a 50 nm aperture fiber based scanning probe microscope. The localized light appears as few to multiple hundreds of nm optical hotspots based on dislocation density of the film. The smaller hotspots correlated to higher defects density as the multi-scattering light is confined in smaller area. The optical collection was done in second harmonic regime to avoid the dominating surface reflection of fundamental light. We used this approach to introduces a noninvasive tabletop technique capable of optically detecting dislocation atomic defects in III-V materials thin films. New diagnosis and supporting simulation would be shown. |
Tuesday, March 3, 2020 3:06PM - 3:18PM |
J10.00004: Native and induced surface charge-transfer doping of MoTe2 Gheorghe Stan, Cristian Ciobanu, Sri Ranga Jai Likith, Asha Rani, Siyuan Zhang, Christina Hacker, Sergiy Krylyuk, Albert Davydov In this work, the surface charge transfer doping (SCTD) of air-exposed MoTe2 surfaces was investigated by means of Kelvin probe force microscopy (KPFM). The induced changes in the surface potential of MoTe2 due to air adsorbates were detailed as a function of the thickness of exfoliated MoTe2 flakes all the way down to a single atomic layer. It was found that the SCTD driven by adsorbates can be easily adjusted through thermal annealing and, in this way, have an effective modulation on the surface potential of MoTe2. Furthermore, the SCTD was controlled locally at the nanoscale by using the probe of an electrostatic force microscope as a floating gate. Following either thermal annealing or contact electrification, the air-exposed MoTe2 surfaces exhibited slow reversal processes of re-adsorption with the restoration of the original positively charged doping. The KPFM measurements were paired with X-ray photoelectron spectroscopy measurements and density functional theory calculations to assess the contribution of physisorbed molecules to the observed variations in the measured surface potential. The study emphasizes that, as a reversible and controllable nanoscale physisorption, the SCTD can add significant progress to the paradigm of emerging 2D-TMDC electronics. |
Tuesday, March 3, 2020 3:18PM - 3:30PM |
J10.00005: A traveling salesperson and sparse sampling boost to quasiparticle interference mapping Jens Oppliger, Fabian Natterer The serial nature of STM investigations render complex measurement tasks, such as QPI mapping, impractical. Conventionally, QPI is carried out by recording hundreds of thousands of point-spectra that capture LDOS modulations from which scattering-space is calculated via Fourier-transform. Yet, despite the efforts required, QPI is mapped extensively because it provides insight into band-structure details when measurement conditions prohibit the use of ARPES. However, in view of the large number of data-points, it is surprising that QPI patterns contain only little information. This motivates our use of compressed sensing (CS) to fundamentally speed-up QPI mapping [1]. From only a fraction of the usual LDOS measurements, we reliably recover the full QPI pattern. Since CS depends on incoherent measurements, we sparsely sample LDOS at randomly selected locations using constant and varying probability density. The STM tip is moved between LDOS measurements according to a traveling salesperson to achieve an overall 5-50 faster QPI throughput. |
Tuesday, March 3, 2020 3:30PM - 3:42PM |
J10.00006: Controlled Manipulation of Molecules: A New Pathway to Catalysis Research Omur Dagdeviren, Chao Zhou, Milica Todorovic, Eric Altman, Udo Dietmar Schwarz With the continued development of scanning probe microscopy techniques, the manipulation of single molecules has become possible. Thereby, the manipulation path can be chosen at will and energy barriers can be quantified, as can the energy landscape around the molecule [1]. The molecules were either pushed, pulled, jumped to the tip, or did not move depending on the chemical surrounding of the molecule and the chemical identity of the tip. To preserve the accuracy of recovered tip-sample interaction, we used oscillation amplitudes significantly larger than the decay length of the tip-sample interaction potential [1-3]. For further insight, we compared measured energy landscapes and manipulation outcomes with computational results obtained using a search protocol [4]. References: [1] O. E. Dagdeviren et al., Nanotechnology 27 (2016). [2] O.E. Dagdeviren et al., Phy. Rev. App. 9 (2018). [3] O. E. Dagdeviren et al., Rev. of Scien. Ins. 90 (2019). [4] M. Todorovic et al. npj Comp Mat. 5 (2019).<div id="UMS_TOOLTIP" style="position: absolute; cursor: pointer; z-index: 2147483647; background-color: transparent; top: -100000px; left: -100000px; background-position: initial initial; background-repeat: initial initial;"> </div> |
Tuesday, March 3, 2020 3:42PM - 3:54PM |
J10.00007: Optimal Bayesian experimental design for everyday measurements Robert McMichael, Sergey Dushenko, Kapildeb Ambal This talk gives an overview of software that increases speed and precision of routine measurements. In experiments where one would traditionally fit data to extract parameters, the ‘optbayesexpt’ package recommends measurement settings “on the fly” based on analysis of accumulating data. The algorithm uses optimal Bayesian experimental design to predict settings with the best chance of reducing parameter uncertainty. In simulations and in tests, we demonstrate order-of-magnitude speedup in measurements of Lorentzian peaks and significant speedup of exponential decay measurements relative to measure-then-fit strategies. The package, written in Python, includes a server script that communicates with instrument control software in any popular instrument control language. Demonstrations include magnetic resonance spectra of NV centers in diamond, and simulations include calibration of π-pulses for spin control. See the manual at https://pages.nist.gov/optbayesexpt/ and software at https://github.com/usnistgov/optbayesexpt. |
Tuesday, March 3, 2020 3:54PM - 4:06PM |
J10.00008: Defect Identification and Statistics Toolbox (DIST): A Tool for Automating Defect Analysis and Statistics Generation Alana Gudinas, Shawna Hollen, Jason P Moscatello Widely available scanning tunneling microscopy (STM) image analysis software do not automate the process of identifying and analyzing atomic defects in images. We present the Defect Identification and Statistics Toolbox (DIST) as a solution for automated defect analysis, based in MATLAB. DIST provides a graphical user interface for interactive image processing and background noise reduction to aid in defect identification. In DIST, topographical contour plots are generated in the image to isolate defects based on their brightness compared to the background. DIST implements an ant colony optimization (ACO) algorithm [1] to compare the shapes of individual defects defined by the contour plots. DIST also automatically compiles and outputs statistics of identified defects, such as apparent height, line profiles, and area. The novel automation techniques in DIST allow users to quickly and accurately analyze hundreds of defects at a time without relying on the user to manually identify each one. |
Tuesday, March 3, 2020 4:06PM - 4:42PM |
J10.00009: Learning hidden structure of nanoscale spectroscopies with metric analysis Invited Speaker: Petro Maksymovych Scanning probe microscopy is routinely faced with analysis of heterogeneous datasets, largely without having reference analytical models. Ordered parts of probed materials, such as regular lattice structures or coherent scattering can often be effectively captured with integral transforms to reveal the atomic and electronic structure, sometimes with unprecedented resolution. However, the problem of capturing inhomogeneities and furthermore understanding of their physical significance demands further attention. Here we will demonstrate how an appropriately chosen metric, combined with analysis of its variability enables effective characterization of key and often hidden structures within hyperspectral datasets. We applied such methodology to three representative cases with remarkably strong inhomogeneity in the nanoscale properties – variability of superconducting gap and electronic structure in unconventional superconductors FeSe and Ba2FeAs2, measurement of individual defects in these systems and structural phase transitions in dipolar solids. In each case, metric analysis has revealed a wide-range of sometimes unexpected properties, such as log-normal distribution in tunneling spectroscopy and sub-surface location of atomic-scale defects, while also providing a robust approach to understand spectral weight transfer and spectral signatures of electronic defects and to detect phase transitions in dielectric solids from their hysteretic response to applied fields. Future extension of these methodologies to combine elements of machine and deep learning will also be discussed. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700