Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning
Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning
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DOI：
10.1021/acsnano.8b02208
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发表时间：
2018-06-01
期刊：
影响因子：
17.1
通讯作者：
Wolkow, Robert A.
中科院分区：
文献类型：
--
作者：
Rashidi, Mohammad;Wolkow, Robert A.
原子尺度的表征和操作与扫描探针显微镜依赖于使用原子尖锐的探针。在这里，我们提出了基于机器学习的自动化方法来自动检测和修复扫描隧道显微镜探针的质量。作为一个模型系统，我们采用这些技术在技术相关的氢终止硅表面，训练网络识别异常的表面悬挂键的外观。在测试的机器学习方法中，卷积神经网络的准确率最高，在97%的测试案例中实现了对降级提示的积极识别。通过采用多点比较和多数表决，该方法的准确性提高到99%以上。
Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases. By using multiple points of comparison and majority voting, the accuracy of the method is improved beyond 99%.