论文标题
通过机器学习快速扫描探针显微镜：通过压缩传感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
论文作者
论文摘要
通过机器学习启用了快速扫描探针显微镜，可以发现各种纳米级的纳米级物理学。但是，此类功能成像的示例数量很少。在这里，使用Piezoresponse力显微镜（PFM）作为模型应用，我们使用稀疏的螺旋扫描与压缩传感和高斯处理重建的组合证明了成像率的5.8倍。发现即使极稀疏的扫描也提供了强大的重建，而高斯处理重建的误差少于6％。此外，我们分析了与每次重建迭代的每种重构技术相关的误差，发现误差与过去15个迭代相似，而在初始迭代时，高斯处理效果优于压缩感应。这项研究突出了重建技术的能力，当应用于稀疏数据，尤其是稀疏的螺旋PFM扫描，并在扫描探针和电子显微镜中进行了广泛的应用。
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.
