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基于自适应模态分解的重叠峰提取用于共焦显微术厚度测量

Adaptive modal decomposition based overlapping-peaks extraction for thickness measurement in chromatic confocal microscopy.

作者信息

Li Jiafu, Zhao Yanlong, Du Hua, Zhu Xiaoping, Wang Kai, Zhao Mo

出版信息

Opt Express. 2020 Nov 23;28(24):36176-36187. doi: 10.1364/OE.410177.

Abstract

Accurate overlapping-peaks extraction plays a critical role in chromatic confocal thickness measurement of ultra-thin transparent film. However, the current algorithms usually appear as a perceptible extraction error resulting from the disturbing influence among peaks in the process of fitting the spectral axial response signal (sARS) of the two measuring surfaces. In this paper, we propose an adaptive modal decomposition method to extract multi peaks for the ultra-thin materials. With this method, the sARS can be firstly decomposed into several sub-modes, which can be used to obtain the peak wavelength of each measuring surface by the existing single peak extraction algorithms, such as the centroid method and Gauss fitting method. Monte Carlo simulations and experimental tests demonstrate that the proposed algorithm has significant improvements over the existing nonlinear fitting algorithms in terms of peak extraction accuracy and precision.

摘要

精确的重叠峰提取在超薄透明薄膜的彩色共焦厚度测量中起着关键作用。然而,当前算法在拟合两个测量表面的光谱轴向响应信号(sARS)过程中,由于峰之间的干扰影响,通常会出现明显的提取误差。在本文中,我们提出了一种自适应模态分解方法来提取超薄材料的多个峰。通过该方法,sARS首先可以分解为几个子模式,然后可利用现有的单峰提取算法(如质心法和高斯拟合方法)来获得每个测量表面的峰值波长。蒙特卡罗模拟和实验测试表明,所提出的算法在峰提取精度和精密度方面比现有的非线性拟合算法有显著改进。

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