Multivariate analysis for scanning tunneling spectroscopy data
Multivariate analysis for scanning tunneling spectroscopy data
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扫描隧道光谱数据的多变量分析
DOI：
10.1016/j.apsusc.2017.09.124
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发表时间：
2018
影响因子：
6.7
通讯作者：
and Daisuke Fujita
中科院分区：
文献类型：
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作者：
Junsuke Yamanishi;Shigeru Iwase;Nobuyuki Ishid;and Daisuke Fujita
我们将主成分分析（PCA）应用于Si（111）-（7 × 7）表面上获得的二维隧道谱（2D）数据，以探索多元分析解释2D隧道谱数据的有效性。我们证明，主要来自Si（111）-（7 × 7）表面特定原子的几种成分可以通过PCA提取。此外，我们表明，隐藏的组件在隧道光谱可以分解（峰分离），这是很难实现正常的2S分析没有理论计算的支持。我们的分析表明，多变量分析可以是一个额外的强大的方法来分析2000年数据和提取隐藏的信息，从大量的光谱数据。
We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.