Multivariate analysis for scanning tunneling spectroscopy data

被引：2
|
作者：
Yamanishi, Junsuke [1 ]
Iwase, Shigeru [2 ]
Ishida, Nobuyuki [3 ]
Fujita, Daisuke [3 ]
机构：
[1] Osaka Univ, Dept Appl Phys, 2-2 Yamadaoka, Suita, Osaka 5650871, Japan
[2] Univ Tsukuba, Grad Sch Pure & Appl Sci, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058571, Japan
[3] Natl Inst Mat Sci, 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, Japan
来源：
APPLIED SURFACE SCIENCE | 2018年 / 428卷
关键词：
Scanning tunneling microscopy; Multivariate analysis; Principal component analysisa; PRINCIPAL COMPONENT ANALYSIS; SI(111)2X1 SURFACE; MICROSCOPY; DECOMPOSITION; NOISE;
D O I：
10.1016/j.apsusc.2017.09.124
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 x 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 x 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. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：186 / 190
页数：5
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