Kelley Kyle P, Ziatdinov Maxim, Collins Liam, Susner Michael A, Vasudevan Rama K, Balke Nina, Kalinin Sergei V, Jesse Stephen
The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, 45433, USA.
Small. 2020 Sep;16(37):e2002878. doi: 10.1002/smll.202002878. Epub 2020 Aug 11.
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. 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.
通过机器学习实现的快速扫描探针显微镜能够揭示广泛的纳米级、时间分辨物理现象。然而，这种功能成像的例子数量很少。在这里，以压电力显微镜（PFM）作为模型应用，展示了使用稀疏螺旋扫描与压缩感知和高斯过程回归重建相结合的数据收集减少了5.8倍。研究发现，即使是极其稀疏的螺旋扫描，高斯过程回归重建的误差也不到6%，能提供强大的重建效果。此外，分析了每次重建迭代中与每种重建技术相关的误差，发现经过约15次迭代后误差相似，而在初始迭代中高斯过程回归优于压缩感知。这项研究突出了重建技术应用于稀疏数据时的能力，特别是稀疏螺旋PFM扫描，在扫描探针和电子显微镜中有广泛应用。
