Automated Tip Conditioning for Scanning Tunneling Spectroscopy
Automated Tip Conditioning for Scanning Tunneling Spectroscopy
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DOI：
10.1021/acs.jpca.0c10731
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发表时间：
2021-02-09
影响因子：
2.9
通讯作者：
Fischer, Felix R.
中科院分区：
文献类型：
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作者：
Wang, Shenkai;Zhu, Junmian;Fischer, Felix R.
扫描隧道光谱 (STS) 是一种记录隧道电流随针尖与样品之间间隙偏压 (dI/dV) 变化的技术，是表征单分子和纳米材料电子结构的有力工具。在执行 STS 时，扫描探针显微镜 (SPM) 尖端的结构和状况对于可靠地获得高质量点光谱至关重要。在这里，我们提出了一个基于机器学习模型的自动化程序，该程序可以识别 dI/dV 点光谱中的 Au(111) 肖克利表面状态，并以最少的用户干预对干净或稀疏覆盖的金表面进行尖端调节。我们采用简单的基于高度的分割算法来分析 STM 形貌图像，以识别尖端调节位置，并使用 1789 个存档的 dI/dV 光谱来训练机器学习模型，该模型可以通过评估光谱数据的质量来确定尖端的状况。基于决策树的集成和提升模型以及深度神经网络 (DNN) 已被证明可以在适合 STS 的条件下可靠地识别提示。我们期望自动化程序能够减少运营成本和时间，提高表面科学研究的可重复性，并加速 STM 新型纳米材料的发现和表征。本文提出的策略可以很容易地适用于各种其他常见基材上的 STM 尖端调节。
Scanning tunneling spectroscopy (STS), a technique that records the change in the tunneling current as a function of the bias (dI/dV) across the gap between a tip and the sample, is a powerful tool to characterize the electronic structure of single molecules and nanomaterials. While performing STS, the structure and condition of the scanning probe microscopy (SPM) tips are critical for reliably obtaining high quality point spectra. Here, we present an automated program based on machine learning models that can identify the Au(111) Shockley surface state in dI/dV point spectra and perform tip conditioning on clean or sparsely covered gold surfaces with minimal user intervention. We employed a straightforward height-based segmentation algorithm to analyze STM topographic images to identify tip conditioning positions and used 1789 archived dI/dV spectra to train machine learning models that can ascertain the condition of the tip by evaluating the quality of the spectroscopic data. Decision tree based ensemble and boosting models and deep neural networks (DNNs) have been shown to reliably identify tips in suitable conditions for STS. We expect the automated program to reduce operational costs and time, increase reproducibility in surface science studies, and accelerate the discovery and characterization of novel nanomaterials by STM. The strategies presented in this paper can readily be adapted to STM tip conditioning on a wide variety of other common substrates.