de Jong T A, Kok D N L, van der Torren A J H, Schopmans H, Tromp R M, van der Molen S J, Jobst J
Huygens-Kamerlingh Onnes Laboratorium, Leiden Institute of Physics, Leiden University, Niels Bohrweg 2, P.O. Box 9504, RA Leiden NL-2300, the Netherlands.
Mathematical Institute, Leiden University, Niels Bohrweg 1, Leiden 23332CA, the Netherlands.
Ultramicroscopy. 2020 Jun;213:112913. doi: 10.1016/j.ultramic.2019.112913. Epub 2019 Nov 23.
For many complex materials systems, low-energy electron microscopy (LEEM) offers detailed insights into morphology and crystallography by naturally combining real-space and reciprocal-space information. Its unique strength, however, is that all measurements can easily be performed energy-dependently. Consequently, one should treat LEEM measurements as multi-dimensional, spectroscopic datasets rather than as images to fully harvest this potential. Here we describe a measurement and data analysis approach to obtain such quantitative spectroscopic LEEM datasets with high lateral resolution. The employed detector correction and adjustment techniques enable measurement of true reflectivity values over four orders of magnitudes of intensity. Moreover, we show a drift correction algorithm, tailored for LEEM datasets with inverting contrast, that yields sub-pixel accuracy without special computational demands. Finally, we apply dimension reduction techniques to summarize the key spectroscopic features of datasets with hundreds of images into two single images that can easily be presented and interpreted intuitively. We use cluster analysis to automatically identify different materials within the field of view and to calculate average spectra per material. We demonstrate these methods by analyzing bright-field and dark-field datasets of few-layer graphene grown on silicon carbide and provide a high-performance Python implementation.
对于许多复杂材料系统，低能电子显微镜（LEEM）通过自然地结合实空间和倒易空间信息，提供了对形态学和晶体学的详细洞察。然而，其独特优势在于所有测量都能轻松地以能量依赖方式进行。因此，为了充分利用这一潜力，应将LEEM测量视为多维光谱数据集而非图像。在此，我们描述一种测量和数据分析方法，以获得具有高横向分辨率的此类定量光谱LEEM数据集。所采用的探测器校正和调整技术能够在强度的四个数量级上测量真实反射率值。此外，我们展示了一种针对具有反转对比度的LEEM数据集量身定制的漂移校正算法，该算法无需特殊计算要求即可产生亚像素精度。最后，我们应用降维技术将包含数百幅图像的数据集的关键光谱特征总结为两张单幅图像，这两张图像能够轻松地直观呈现和解读。我们使用聚类分析自动识别视野内的不同材料，并计算每种材料的平均光谱。我们通过分析在碳化硅上生长的少层石墨烯的明场和暗场数据集来演示这些方法，并提供了一个高性能的Python实现。
