Embedding human heuristics in machine-learning-enabled probe microscopy
Embedding human heuristics in machine-learning-enabled probe microscopy
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DOI：
10.1088/2632-2153/ab42ec
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发表时间：
2020-03-01
影响因子：
6.8
通讯作者：
Moriarty, Philip J.
中科院分区：
文献类型：
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作者：
Gordon, Oliver M.;Junqueira, Filipe L. Q.;Moriarty, Philip J.
扫描探针显微镜通常不依赖于完整的图像来评估扫描过程中获得的数据的质量。相反，尖端顶点的状态的评估（其不仅确定任何扫描探针技术中的分辨率，而且还可以产生各种各样的令人沮丧的伪影）是基于图像的几条线（并且通常是它们相关联的线轮廓）在真实的时间内进行的。然而，迄今为止发表的用于探测显微镜的机器学习方法数量很少，涉及基于完整图像的分类。鉴于数据采集是常规尖端调节过程中最耗时的任务，因此与探针显微镜工作者常规使用的久经考验的策略和方法相比，自动化方法目前非常缓慢。在这里，我们探索不同的STM图像类（所产生的尖端状态的变化）可以正确地识别部分扫描的各种策略。通过采用一个次级的时间网络和一个滚动窗口的一小群个人扫描线，我们发现，提示评估是可能的一个完整的图像的一小部分。我们实现了这一点，几乎没有性能损失，或者，事实上，在某些情况下，显着提高性能，并介绍了一个协议，以检测状态的尖端顶点在真实的时间。
Scanning probe microscopists generally do not rely on complete images to assess the quality of data acquired during a scan. Instead, assessments of the state of the tip apex, which not only determines the resolution in any scanning probe technique, but can also generate a wide array of frustrating artefacts, are carried out in real time on the basis of a few lines of an image (and, typically, their associated line profiles.) The very small number of machine learning approaches to probe microscopy published to date, however, involve classifications based on full images. Given that data acquisition is the most time-consuming task during routine tip conditioning, automated methods are thus currently extremely slow in comparison to the tried-and-trusted strategies and heuristics used routinely by probe microscopists. Here, we explore various strategies by which different STM image classes (arising from changes in the tip state) can be correctly identified from partial scans. By employing a secondary temporal network and a rolling window of a small group of individual scanlines, we find that tip assessment is possible with a small fraction of a complete image. We achieve this with little-to-no performance penalty-or, indeed, markedly improved performance in some cases-and introduce a protocol to detect the state of the tip apex in real time.