Dictionary learning in Fourier-transform scanning tunneling spectroscopy
Dictionary learning in Fourier-transform scanning tunneling spectroscopy
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DOI：
10.1038/s41467-020-14633-1
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发表时间：
2020-02-26
影响因子：
16.6
通讯作者：
Pasupathy, Abhay N.
中科院分区：
文献类型：
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作者：
Cheung, Sky C.;Shin, John Y.;Pasupathy, Abhay N.
现代高分辨率显微镜通常用于研究具有密集和非周期性空间结构的标本。从这种显微镜获得的图像中提取有意义的信息仍然是一个艰巨的挑战。傅立叶分析通常用于分析这种图像的结构。然而，傅里叶变换从根本上遭受严重的相位噪声时，应用于非周期性的图像。在这里，我们报告了一个基于非凸优化的算法的发展，直接揭示了存在于真实空间图像中的基本图案。除了在定量上上级传统的傅立叶分析，我们表明，该算法还揭示了相位敏感信息的基本图案结构。我们通过研究Co掺杂的铁砷化物超导体的扫描隧道显微镜图像证明了它的实用性，并证明了该算法的应用允许在这种材料中的准粒子干扰的完全恢复。
Modern high-resolution microscopes are commonly used to study specimens that have dense and aperiodic spatial structure. Extracting meaningful information from images obtained from such microscopes remains a formidable challenge. Fourier analysis is commonly used to analyze the structure of such images. However, the Fourier transform fundamentally suffers from severe phase noise when applied to aperiodic images. Here, we report the development of an algorithm based on nonconvex optimization that directly uncovers the fundamental motifs present in a real-space image. Apart from being quantitatively superior to traditional Fourier analysis, we show that this algorithm also uncovers phase sensitive information about the underlying motif structure. We demonstrate its usefulness by studying scanning tunneling microscopy images of a Co-doped iron arsenide superconductor and prove that the application of the algorithm allows for the complete recovery of quasiparticle interference in this material.