Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data
Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data
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DOI：
10.1103/physrevapplied.15.014001
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发表时间：
2021-01-04
影响因子：
4.6
通讯作者：
Liu, Mengkun
中科院分区：
文献类型：
--
作者：
Chen, Xinzhong;Ren, Richard;Liu, Mengkun
散射式扫描近场光学显微镜是一种用于亚衍射光学成像和光谱分析的现代技术。在过去的二十年里，人们投入了巨大的努力来模拟复杂的针尖-样品相互作用，目的是在纳米尺度上理解材料的电动力学。然而，由于分析方法的复杂性和完全数值模拟的计算能力有限，必须做出妥协以促进尖端-样品相互作用的建模，例如使用准静态近似或不现实的尖端几何形状。在本文中，我们应用了各种广泛使用的机器学习方法，包括k近邻和前馈神经网络等来研究相分辨光谱的近场响应。利用有限偶极子模型模拟的一小组训练数据，我们证明了实验近场信号与样品光学常数之间的关系可以一一映射，而不需要尖端建模：对于给定的介电函数适中的材料，可以在中红外光谱范围内准确地确定其复近场光谱，反之亦然。我们的初步研究为未来使用真实的实验数据进行探索奠定了基础。我们的方法对于处理许多研究小组积累的越来越多的数据是有益的，特别是对于用户设施，如基于同步加速器的国家实验室，其中每天产生大量数据。
Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. In this paper, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feed-forward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.