Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Kelley, Kyle P.; Ziatdinov, Maxim; Collins, Liam; Susner, Michael A.; Vasudevan, Rama K.; Balke, Nina; Kalinin, Sergei V.; Jesse, Stephen

doi:10.1002/smll.202002878

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Journal Article · Tue Aug 11 00:00:00 EDT 2020 · Small

DOI:https://doi.org/10.1002/smll.202002878· OSTI ID:1649231

^[1]; ^[1]; ^[1]; Susner, Michael A. ^[2]; ^[1]; ^[1]; ^[1]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Wright‐Patterson Air Force Base Dayton, OH (United States)

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. Finally, this study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1649231

Alternate ID(s):: OSTI ID: 1647248

Journal Information:: Small, Journal Name: Small Journal Issue: 37 Vol. 16; ISSN 1613-6810

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
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Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

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