论文标题
使用高光谱成像和机器学习来表征过渡金属二进制二甲化合物薄膜
Characterizing Transition-Metal Dichalcogenide Thin-Films using Hyperspectral Imaging and Machine Learning
论文作者
论文摘要
原子上薄的多晶转化金属元素化（TMDS）与基本科学研究和应用都相关。 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，我们提出了一种快速表征使用纳米晶膜的纳米晶粒结构和纹理的方法，并使用扫描纳米氨基电子衍射以及所得数据的多变量统计分析。我们的分析管道是高度概括的，是传统上用于分析空间分析的电子衍射测量的耗时，复杂和系统依赖方法的有用替代方法。
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 WS2 films 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.
