Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization
Abstract
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.
- Authors:
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[1];
[1];
[1];
[1];
[1];
[1];
[1]
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Wright‐Patterson Air Force Base Dayton, OH (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1649231
- Alternate Identifier(s):
- OSTI ID: 1647248
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Small
- Additional Journal Information:
- Journal Volume: 16; Journal Issue: 37; Journal ID: ISSN 1613-6810
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; Piezoresponse force microscopy; atomic force microscopy; Gaussian process regression; compressive sensing; ferroelectric; ferroelectric heterostructures
Citation Formats
Kelley, Kyle P., Ziatdinov, Maxim, Collins, Liam, Susner, Michael A., Vasudevan, Rama K., Balke, Nina, Kalinin, Sergei V., and Jesse, Stephen. Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization. United States: N. p., 2020.
Web. doi:10.1002/smll.202002878.
Kelley, Kyle P., Ziatdinov, Maxim, Collins, Liam, Susner, Michael A., Vasudevan, Rama K., Balke, Nina, Kalinin, Sergei V., & Jesse, Stephen. Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization. United States. https://doi.org/10.1002/smll.202002878
Kelley, Kyle P., Ziatdinov, Maxim, Collins, Liam, Susner, Michael A., Vasudevan, Rama K., Balke, Nina, Kalinin, Sergei V., and Jesse, Stephen. Tue .
"Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization". United States. https://doi.org/10.1002/smll.202002878. https://www.osti.gov/servlets/purl/1649231.
@article{osti_1649231,
title = {Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization},
author = {Kelley, Kyle P. and Ziatdinov, Maxim and Collins, Liam and Susner, Michael A. and Vasudevan, Rama K. and Balke, Nina and Kalinin, Sergei V. and Jesse, Stephen},
abstractNote = {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.},
doi = {10.1002/smll.202002878},
journal = {Small},
number = 37,
volume = 16,
place = {United States},
year = {Tue Aug 11 00:00:00 EDT 2020},
month = {Tue Aug 11 00:00:00 EDT 2020}
}
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Works referencing / citing this record:
Compressed Sensing for STM imaging of defects and disorder
text, January 2021
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