High-veracity functional imaging in scanning probe microscopy via Graph-Bootstrapping

Xin Li, Liam Collins, Keisuke Miyazawa, Takeshi Fukuma, Stephen Jesse and Sergei V. Kalinin ()
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Xin Li: Oak Ridge National Laboratory
Liam Collins: Oak Ridge National Laboratory
Keisuke Miyazawa: Kanazawa University
Takeshi Fukuma: Kanazawa University
Stephen Jesse: Oak Ridge National Laboratory
Sergei V. Kalinin: Oak Ridge National Laboratory

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: Abstract The key objective of scanning probe microscopy (SPM) techniques is the optimal representation of the nanoscale surface structure and functionality inferred from the dynamics of the cantilever. This is particularly pertinent today, as the SPM community has seen a rapidly growing trend towards simultaneous capture of multiple imaging channels and complex modes of operation involving high-dimensional information-rich datasets, bringing forward the challenges of visualization and analysis, particularly for cases where the underlying dynamic model is poorly understood. To meet this challenge, we present a data-driven approach, Graph-Bootstrapping, based on low-dimensional manifold learning of the full SPM spectra and demonstrate its successes for high-veracity mechanical mapping on a mixed polymer thin film and resolving irregular hydration structure of calcite at atomic resolution. Using the proposed methodology, we can efficiently reveal and hierarchically represent salient material features with rich local details, further enabling denoising, classification, and high-resolution functional imaging.

Date: 2018
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DOI: 10.1038/s41467-018-04887-1

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