期刊
SMALL
卷 16, 期 37, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202002878
关键词
atomic force microscopy; compressive sensing; ferroelectric heterostructures; Gaussian process regression; piezoresponse force microscopy
类别
资金
- U.S. Department of Energy, Office of Science, Materials Sciences and Engineering Division
- AFOSR [19RXCOR052]
- AFRL/RX (Lab Director's Funds)
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 approximate to 15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. 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.
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