通过机器学习的快速扫描探针显微镜：具有压缩感知和高斯过程优化的非矩形扫描
Fast Scanning Probe Microscopy via Machine Learning: Non-rectangular scans with compressed sensing and Gaussian process optimization
摘要：
通过机器学习实现的快速扫描探针显微镜允许揭示广泛的纳米级时间分辨物理学。然而，这种功能成像的例子很少。在这里，使用piezoresponse力显微镜（PFM）作为一个模型的应用程序，我们证明了一个因素的5.8改善成像速率使用稀疏螺旋扫描与压缩传感和高斯处理重建相结合。据发现，即使是非常稀疏的扫描提供强重建与高斯处理重建的误差小于6%。此外，我们分析了每次重建迭代与每种重建技术相关的误差，发现误差在大约15次迭代之后是相似的，而在初始迭代时，高斯处理优于压缩感知。这项研究突出了重建技术的能力时，应用于稀疏数据，特别是稀疏螺旋PFM扫描，扫描探针和电子显微镜的广泛应用。
英文摘要：
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, we demonstrate a factor of 5.8 improvement in imaging rate using a combination of sparse spiral scanning with compressive sensing and Gaussian processing reconstruction. It is found that even extremely sparse scans offer strong reconstructions with less than 6 % error for Gaussian processing reconstructions. Further, we analyze the error associated with each reconstructive technique per reconstruction iteration finding the error is similar past approximately 15 iterations, while at initial iterations Gaussian processing 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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