Characterizing transition-metal dichalcogenide thin-films using hyperspectral imaging and machine learning
Characterizing transition-metal dichalcogenide thin-films using hyperspectral imaging and machine learning
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DOI：
10.1038/s41598-020-68321-7
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发表时间：
2020-01
影响因子：
4.6
通讯作者：
Brian Shevitski;Christopher T. Chen;C. Kastl;T. Kuykendall;A. Schwartzberg;S. Aloni;A. Zettl
中科院分区：
文献类型：
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作者：
Brian Shevitski;Christopher T. Chen;C. Kastl;T. Kuykendall;A. Schwartzberg;S. Aloni;A. Zettl
原子级薄的过渡金属二硫族化合物（TMD）多晶材料在基础科学研究和应用领域都具有重要意义。TMD薄膜对有效的纳米级晶体表征提出了独特的困难挑战。在这里，我们提出了一种方法来快速表征纳米晶粒结构和单层WS2薄膜的纹理，使用扫描纳米束电子衍射与多元统计分析的结果数据相结合。我们的分析管道具有高度的通用性，是传统上用于分析空间分辨电子衍射测量的耗时，复杂和依赖系统的方法的有用替代方案。
Atomically thin polycrystalline transition-metal dichalcogenides (TMDs) are relevant to both fundamental science investigation and applications. TMD thin-films present uniquely difficult challenges to effective nanoscale crystalline characterization. Here we present a method to quickly characterize the nanocrystalline grain structure and texture of monolayer WS2films using scanning nanobeam electron diffraction coupled with multivariate statistical analysis of the resulting data. Our analysis pipeline is highly generalizable and is a useful alternative to the time consuming, complex, and system-dependent methodology traditionally used to analyze spatially resolved electron diffraction measurements.