Jones Lewys, Wang Shuqiu, Hu Xiao, Ur Rahman Shams, Castell Martin R
1Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH UK.
2Present Address: School of Physics & CRANN, Trinity College Dublin, Dublin 2, Ireland.
Adv Struct Chem Imaging. 2018;4(1):7. doi: 10.1186/s40679-018-0056-7. Epub 2018 Jun 7.
The usual way to present images from a scanning tunneling microscope (STM) is to take multiple images of the same area, to then manually select the one that appears to be of the highest quality, and then to discard the other almost identical images. This is in contrast to most other disciplines where the signal to noise ratio (SNR) of a data set is improved by taking repeated measurements and averaging them. Data averaging can be routinely performed for 1D spectra, where their alignment is straightforward. However, for serial-acquired 2D STM images the nature and variety of image distortions can severely complicate accurate registration. Here, we demonstrate how a significant improvement in the resolving power of the STM can be achieved through automated distortion correction and multi-frame averaging (MFA) and we demonstrate the broad utility of this approach with three examples. First, we show a sixfold enhancement of the SNR of the Si(111)-(7 × 7) reconstruction. Next, we demonstrate that images with sub-picometre height precision can be routinely obtained and show this for a monolayer of TiO on Au(111). Last, we demonstrate the automated classification of the two chiral variants of the surface unit cells of the (4 × 4) reconstructed SrTiO(111) surface. Our new approach to STM imaging will allow a wealth of structural and electronic information from surfaces to be extracted that was previously buried in noise.
呈现扫描隧道显微镜（STM）图像的常规方法是对同一区域拍摄多张图像，然后手动选择看起来质量最高的那张，接着丢弃其他几乎相同的图像。这与大多数其他学科不同，在那些学科中，通过重复测量并求平均值来提高数据集的信噪比（SNR）。对于一维光谱，数据平均可以常规进行，因为它们的对齐很简单。然而，对于串行采集的二维STM图像，图像失真的性质和种类会使精确配准严重复杂化。在这里，我们展示了如何通过自动失真校正和多帧平均（MFA）实现STM分辨能力的显著提高，并通过三个例子展示了这种方法的广泛实用性。首先，我们展示了Si(111)-(7×7)重构的信噪比提高了六倍。其次，我们证明了可以常规获得具有亚皮米高度精度的图像，并以Au(111)上的单层TiO为例进行了展示。最后，我们展示了(4×4)重构的SrTiO(111)表面的表面晶胞的两种手性变体的自动分类。我们新的STM成像方法将能够提取大量以前被噪声掩盖的来自表面的结构和电子信息。
