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基于机器学习的过焦扫描显微测量方法研究

李冠楠 石俊凯 陈晓梅 高超 姜行健 崔成君 朱强 霍树春 周维虎

李冠楠, 石俊凯, 陈晓梅, 高超, 姜行健, 崔成君, 朱强, 霍树春, 周维虎. 基于机器学习的过焦扫描显微测量方法研究[J]. 中国光学(中英文), 2022, 15(4): 703-711. doi: 10.37188/CO.2022-0009
引用本文: 李冠楠, 石俊凯, 陈晓梅, 高超, 姜行健, 崔成君, 朱强, 霍树春, 周维虎. 基于机器学习的过焦扫描显微测量方法研究[J]. 中国光学(中英文), 2022, 15(4): 703-711. doi: 10.37188/CO.2022-0009
LI Guan-nan, SHI Jun-kai, CHEN Xiao-mei, GAO Chao, JIANG Xing-jian, CUI Cheng-jun, ZHU Qiang, HUO Shu-chun, ZHOU Wei-hu. Through-focus scanning optical microscopy measurement based on machine learning[J]. Chinese Optics, 2022, 15(4): 703-711. doi: 10.37188/CO.2022-0009
Citation: LI Guan-nan, SHI Jun-kai, CHEN Xiao-mei, GAO Chao, JIANG Xing-jian, CUI Cheng-jun, ZHU Qiang, HUO Shu-chun, ZHOU Wei-hu. Through-focus scanning optical microscopy measurement based on machine learning[J]. Chinese Optics, 2022, 15(4): 703-711. doi: 10.37188/CO.2022-0009

基于机器学习的过焦扫描显微测量方法研究

doi: 10.37188/CO.2022-0009
基金项目: 国家重点研发计划(No. 2019YFB2005603);清华大学精密测试技术及仪器国家重点实验室开放基金(No. TH20-01);国家自然科学基金(No. 51905528);精密测试及仪器国家重点实验室开放基金(No. pilab2102)
详细信息
    作者简介:

    李冠楠(1994—),女,内蒙古赤峰人,硕士,助理工程师,2017年于哈尔滨工业大学获得光电信息科学与工程专业学士学位,2020年于中国科学院大学获得光学工程硕士学位,主要从事微纳测量和光学检测方面的研究。E-mail:liguannan@ime.ac.cn

    石俊凯(1986—),男,天津宁河人,博士,副研究员,2015年于天津大学精仪学院获得博士学位,主要从事光学精密测量、微纳检测及飞秒激光方面的研究。E-mail:shijunkai@ime.ac.cn

  • 中图分类号: TP181;P207+.1;TH744

Through-focus scanning optical microscopy measurement based on machine learning

Funds: Supported by the National Key R&D Program of China (No. 2019YFB2005603); the National Key Laboratory of Precision Testing Techniques and Instrument (Tsinghua University) (No. TH20-01); the National Natural Science Foundation of China (No. 51905528); State Key Laboratory of Precision Measuring Technology and Instruments (Tianjin University) (No. pilab2102)
More Information
  • 摘要:

    微电子机械系统(Micro-Electro-Mechanical System,MEMS)具有小型化、高集成度的特点,随着MEMS结构深宽比的不断增大,对MEMS结构尺寸的测量提出更高的要求。过焦扫描光学显微技术(Through-focus Scanning Optical Microscopy,TSOM)是一种高精度无损的光学测量方法,通过采集一组离焦图并沿扫描方向截取TSOM图像,利用库匹配的方法从中提取待测结构的尺寸信息。该方法对于纳米级结构测量有着极高的灵敏度,然而对于微米级特征尺寸存在建库困难且易受环境干扰的问题。本文针对微米级MEMS沟槽结构,在传统的光学显微镜基础上进行改造,建立了TSOM光学系统采集离焦图像,利用图像特征提取方法生成TSOM特征向量集,结合机器学习的方法建立不同槽宽尺寸的回归预测模型,对微米级MEMS槽宽尺寸实现纳米级测量精度,单点重复性测量2 μm槽宽的相对标准差(Relative Standard Deviation,RSD)在1%左右,10 μm和30 μm槽宽RSD分别低于0.2%和0.35%,结果表明该方法对于微米级MEMS沟槽测量具有极高的应用前景。

     

  • 图 1  TSOM流程示意图

    Figure 1.  Schematic diagram of the TSOM process

    图 2  (a)TSOM实验装置;(b)、(c)、(d)槽宽2 μm、10 μm、30 μm的TSOM伪彩图,其中横向对应像素点,纵向对应离焦扫描位置

    Figure 2.  (a) TSOM experimental setup; TSOM pseudo color images with widths of (b) 2 μm, (c) 10 μm, and (d) 30 μm. The lateral axis represents pixels and the vertical axis represents through-focus positions

    图 3  基于HOG-SVR的MEMS槽宽预测流程图

    Figure 3.  MEMS widths prediction flowchart based on HOG-SVR

    图 4  HOG特征提取

    Figure 4.  HOG feature extraction

    图 5  SVR原理图

    Figure 5.  Schematic diagram of SVR

    图 6  数据集获取流程

    Figure 6.  Processes of dataset acquirement

    图 7  不同槽宽和槽深的MEMS沟槽基于SVR的预测结果

    Figure 7.  Prediction results of MEMS groove with different widths and depths based on SVR

    图 8  模型预测性能评价指标

    Figure 8.  Evaluation indicators of prediction performance of the model

    图 9  模型单点重复性预测结果

    Figure 9.  Prediction results of single point repeatability of the mode

    表  1  样品参数

    Table  1.   Sample parameters

    样品编号设计槽宽/μm槽深/μm深宽比电镜实测槽宽/μm
    122412∶12.21/2.52/2.61/2.86/3.06
    22200100∶11.79/1.98/2.19/2.58
    310343.4∶110.5/10.7/10.8/11.1/11.3
    41010610.6∶110.8/11/11.3/11.7
    530381.3∶130.6/30.9/31/31.2/31.5
    6302367.9∶131.4/31.8/32.1/33.1
    下载: 导出CSV
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  • 收稿日期:  2022-01-10
  • 修回日期:  2022-03-03
  • 网络出版日期:  2022-05-16

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