Auto-Focusing of Ultra-clean Sample Based on Projection View Method

被引：3
作者：
Liu, Xing [1 ,2 ]
Chen, Guodong [1 ,2 ]
Zheng, Jinlun [1 ,2 ]
Wei, Jingsong [1 ,2 ]
机构：
[1] Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Lab Micronano Optoelect Mat & Devices, Shanghai 201800, Peoples R China
[2] Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China
来源：
TWELFTH INTERNATIONAL CONFERENCE ON INFORMATION OPTICS AND PHOTONICS (CIOP 2021) | 2021年 / 12057卷
基金：
中国国家自然科学基金;
关键词：
Auto-focusing; Projection view; Ultra-clean sample;
D O I：
10.1117/12.2606335
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Auto-focusing methods, including infrared ranging, laser triangulation, ultrasound distance measurement, contrast detection, phase detection, depth from defocus, and depth from focus, are widely applied in laser direct writing system. Concurrently, the samples with an ultra-clean surface may cause the focusing to fail. This paper presents a method based on projection view to solve the auto-focusing problem of ultra-clean sample. A projection view in the Koller illumination path, is projected to the focal plane of the objective lens. One can, use the image of projection view in the charge coupled device (CCD) to evaluate the image definition so as to conduct a fast and accurate auto-focusing. A gray variance fusion model used for focusing accuracy evaluation is proposed. The robustness and accuracy of the auto-focusing are measured experimentally. The influencing factors including illumination condition and sample materials are analyzed. This method is useful for auto-focusing of ultra-clean samples.
引用
收藏
页数：7
相关论文
共 13 条
  • [1] Improving the accuracy and low-light performance of contrast-based auto focus using supervised machine learning
    Chen, Rudi
    van Beek, Peter
    [J]. PATTERN RECOGNITION LETTERS, 2015, 56 : 30 - 37
  • [2] Multiple reflection suppression algorithm for terahertz quasi-optic systems
    Davis, J. G.
    Truscott, W. S.
    [J]. ELECTRONICS LETTERS, 2010, 46 (01) : 52 - 53
  • [3] Ge Yunhao, 2019, 2019 9 INT C
  • [4] Improving the performances of autofocus based on adaptive retina-like sampling model
    Hao, Qun
    Xiao, Yuqing
    Cao, Jie
    Cheng, Yang
    Sun, Ce
    [J]. OPTICS COMMUNICATIONS, 2018, 410 : 269 - 276
  • [5] Autofocusing Airy beam STED microscopy with long focal distance
    Hu, Di
    Liang, Yao
    Chen, Yin
    Chen, Zan Hui
    Huang, Xu Guang
    [J]. OPTICS COMMUNICATIONS, 2017, 404 : 196 - 202
  • [6] DeepFocus: A deep learning model for focusing microscope systems
    Ivanov, Toni
    Kumar, Ayush
    Sharoukhov, Denis
    Ortega, Francis
    Putman, Matthew
    [J]. APPLICATIONS OF MACHINE LEARNING 2020, 2020, 11511
  • [7] Comparison of spectral CT imaging methods based a photon-counting detector: Experimental study
    Lee, Youngjin
    Lee, Seungwan
    Kim, Hee-Joung
    [J]. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2016, 815 : 68 - 74
  • [8] Learning to autofocus based on Gradient Boosting Machine for optical microscopy
    Liang, Yixiong
    Yan, Meng
    Tang, Zhihong
    He, Zhujun
    Liu, Jianfeng
    [J]. OPTIK, 2019, 198
  • [9] Design of a flexure-based auto-focusing device for a microscope
    Liu, Yilin
    Xu, Qingsong
    [J]. INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2015, 16 (11) : 2271 - 2279
  • [10] Half-sweep imaging for depth from defocus
    Matsui, Shuhei
    Nagahara, Hajime
    Taniguchi, Rin-ichiro
    [J]. IMAGE AND VISION COMPUTING, 2014, 32 (11) : 954 - 964
12