太赫兹测厚技术中的层数分类算法  

Layer Classification Algorithm in Terahertz Thickness Measurement Technology

作　　者：林杰 齐济[1,2] 张宇奇 张为[2] 陈雨昂[1] 何明霞 曲秋红[2] 张逸竹 Lin Jie;Qi Ji;Zhang Yuqi;Zhang Wei;Chen Yuang;He Mingxia;Qu Qiuhong;Zhang Yizhu(School of Precision Instrument and Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China;Sichuan Innovation Research Institute of Tianjin University,Chengdu 610213,Sichuan,China;School of Electronics and Information Engineering,Tiangong University,Tianjin 300387,China)

机构地区：[1]天津大学精密仪器与光电子工程学院,天津300072 [2]天津大学四川创新研究院,四川成都610213 [3]天津工业大学电子与信息工程学院,天津300387

出　　处：《中国激光》2024年第18期207-216,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(12174284);天津大学自主创新基金(2023XZH-009)。

摘　　要：太赫兹测厚技术因其高精度和非接触性,已成为测量多涂层结构厚度的新途径。通过太赫兹时域光谱技术确定涂层厚度时,通常使用全局优化算法,并需要预先知道涂层的层数。然而在某些应用中,层数往往是未知的,这影响了太赫兹测厚技术的适用性和精度。通过优化支持向量机模型确定涂层层数,并比较不同改进分类算法的性能。确定层数后,利用Rouard模型对每层厚度进行全局拟合来确定涂层厚度,从而实现对未知层数的多涂层结构的分层厚度测量。在机械臂驱动的太赫兹时域光谱系统对曲面多涂层样品进行测厚的实验中验证了所提方法的可行性。Objective Terahertz(THz)thickness measurement technology is widely recognized for its high precision and non-contact nature,making it a novel method for measuring the thicknesses of multi-layer structures.However,because the number of layers in some applications is often unknown,the accuracy and applicability of THz measurements are affected,and this poses a challenge to the technology’s further development.In many industrial scenarios,accurately determining the layer thickness is critical for quality control and material characterization.This study attempts to address this issue by optimizing a support vector machine(SVM)model to determine the number of layers and comparing the performances of different improved classification algorithms.The ultimate goal is to enhance the precision and applicability of THz thickness measurements in multi-layer structures with unknown layers,thereby improving the reliability of measurements in various industrial applications.Methods The proposed method used THz time-domain spectroscopy(THz-TDS)for thickness measurements.The kernel principal component analysis(KPCA)technique was first employed to extract THz spectral features.KPCA helped to reduce the dimensionality of the data while preserving the most informative features,thereby enhancing the performance of the classification model.Various advanced algorithms were then utilized to optimize the SVM for layer classification,including the grid search(GS),sparrow search algorithm(SSA),improved sparrow search algorithm(ISSA),and whale optimization algorithm(WOA).The optimal parameters for the SVM,including the penalty factor(C)and radial basis function(RBF)kernel parameter(g),were determined using these optimization techniques.The performances of these algorithms were then evaluated using five-fold cross-validation to ensure robustness and reliability.Following layer classification,the Rouard model was applied to globally fit each layer’s thickness.This model,coupled with the genetic algorithm for parameter optimization,enabled

关 键 词：太赫兹时域光谱技术 厚度测量 分类算法 多涂层结构 优化支持向量机 

分 类 号：O433.1[机械工程—光学工程] TG174.461[理学—光学]