Barker Dylan Stewart, Sweetman Adam
School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, United Kingdom.
Beilstein J Nanotechnol. 2025 Aug 18;16:1367-1379. doi: 10.3762/bjnano.16.99. eCollection 2025.
Atomic resolution scanning probe microscopy, and in particular scanning tunnelling microscopy (STM) allows for high-spatial-resolution imaging and also spectroscopic analysis of small organic molecules. However, preparation and characterisation of the probe apex in situ by a human operator is one of the major barriers to high-throughput experimentation and to reproducibility between experiments. Characterisation of the probe apex is usually accomplished via assessment of the imaging quality on the target molecule and also the characteristics of the scanning tunnelling spectra (STS) on clean metal surfaces. Critically for spectroscopic experiments, assessment of the spatial resolution of the image is not sufficient to ensure a high-quality tip for spectroscopic measurements. The ability to automate this process is a key aim in development of high resolution scanning probe materials characterisation. In this paper, we assess the feasibility of automating the assessment of imaging quality, and spectroscopic tip quality, via both machine learning (ML) and deterministic methods (DM) using a prototypical tin phthalocyanine on Au(111) system at 4.7 K. We find that both ML and DM are able to classify images and spectra with high accuracy, with only a small amount of prior surface knowledge. We highlight the practical advantage of DM not requiring large training datasets to implement on new systems and demonstrate a proof-of-principle automated experiment that is able to repeatedly prepare the tip, identify molecules of interest, and perform site-specific STS experiments using DM, in order to produce large numbers of spectra with different tips suitable for statistical analysis. Deterministic methods can be easily implemented to classify the imaging and spectroscopic quality of a STM tip for the purposes of high-resolution STM and STS on small organic molecules. Via automated classification of the tip state, we demonstrate an automated experiment that can collect a high number of spectra on multiple molecules without human intervention. The technique can be easily extended to most metal-adsorbate systems and is promising for the development of automated, high-throughput, STM characterisation of small adsorbate systems.
原子分辨率扫描探针显微镜，特别是扫描隧道显微镜（STM），能够对小分子有机化合物进行高空间分辨率成像以及光谱分析。然而，由人工操作员在原位制备和表征探针尖端是高通量实验以及实验间再现性的主要障碍之一。探针尖端的表征通常是通过评估目标分子上的成像质量以及清洁金属表面上的扫描隧道谱（STS）特征来完成的。对于光谱实验而言，至关重要的是，仅评估图像的空间分辨率不足以确保获得用于光谱测量的高质量探针尖端。实现这一过程自动化的能力是高分辨率扫描探针材料表征技术发展的关键目标。在本文中，我们使用处于4.7 K温度下的Au(111)表面上的典型酞菁锡体系，通过机器学习（ML）和确定性方法（DM）来评估成像质量和光谱探针质量评估自动化的可行性。我们发现ML和DM都能够在仅具备少量先验表面知识的情况下，高精度地对图像和光谱进行分类。我们强调了DM的实际优势，即它在新系统上实施时不需要大量训练数据集，并展示了一个原理验证自动化实验，该实验能够使用DM重复制备探针尖端、识别感兴趣的分子并进行特定位置的STS实验，以便产生大量具有不同探针尖端且适合统计分析的光谱。确定性方法可以很容易地用于对STM探针的成像和光谱质量进行分类，以用于对小分子有机化合物进行高分辨率STM和STS研究。通过对探针状态的自动分类，我们展示了一个无需人工干预就能在多个分子上收集大量光谱的自动化实验。该技术可以很容易地扩展到大多数金属 - 吸附质体系，并且对于开发自动化、高通量的小分子吸附质体系的STM表征技术具有广阔前景。
