液晶屏线路中导电粒子压合的自动光学检测研究

陈玉叶; 肖可; 郭振雄; 何俊杰; 刘畅; 陈松岩

doi:10.3788/YJYXS20173207.0553

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液晶屏线路中导电粒子压合的自动光学检测研究

材料科学 | 更新时间：2020-08-12

- 液晶屏线路中导电粒子压合的自动光学检测研究
- Detection of conducting particles bonding in the circuit of liquid crystal display
- 液晶与显示 2017年32卷第7期页码：553-559
- 作者机构：
  
  厦门大学物理科学与技术学院, 福建厦门 361001
- 作者简介：
  
  [ "陈玉叶 (1992－), 女, 湖北广水人, 硕士研究生, 主要从事机器视觉及工业检测中图像处理应用方面的研究。E-mail: yychen@stu.xmu.edu.cn" ]
  [ "陈松岩 (1966－), 男, 黑龙江省黑河人, 教授, 博士生导师, 主要从事硅基光电材料与器件和工业视觉检测研究。E-mail:sychen@xmu.edu.cn" ]
- 基金信息：
  
  福建省高校产学合作项目(No.2016H6026);Supported by Programs for Industry-university Cooperation of Fujian Province, China(No.2016H6026)
- DOI：10.3788/YJYXS20173207.0553
  中图分类号： TP29
- 收稿日期：2017-01-16，
  
  录用日期：2017-3-16，
  
  纸质出版日期：2017-07-05
- 稿件说明：
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陈玉叶, 肖可, 郭振雄, 等. 液晶屏线路中导电粒子压合的自动光学检测研究[J]. 液晶与显示, 2017,32(7):553-559.

Yu-ye CHEN, Ke XIAO, Zhen-xiong GUO, et al. Detection of conducting particles bonding in the circuit of liquid crystal display[J]. Chinese journal of liquid crystals and displays, 2017, 32(7): 553-559.
陈玉叶, 肖可, 郭振雄, 等. 液晶屏线路中导电粒子压合的自动光学检测研究[J]. 液晶与显示, 2017,32(7):553-559. DOI： 10.3788/YJYXS20173207.0553.

Yu-ye CHEN, Ke XIAO, Zhen-xiong GUO, et al. Detection of conducting particles bonding in the circuit of liquid crystal display[J]. Chinese journal of liquid crystals and displays, 2017, 32(7): 553-559. DOI： 10.3788/YJYXS20173207.0553.

摘要

在薄膜晶体管液晶显示器线路检测中，常通过对线路中的导电薄膜粒子的计数和定位实现其导电性的自动检测。为了解决窄边框线路中粒子密度增大带来的粒子重叠问题，提出一种采用微分干涉成像和掩模法结合k均值聚类的算法，在分离出粒子的亮、暗部后，结合图像熵值和粒子的凸性准确分割出粒子。讨论了聚类簇选值的影响，通过不同粒子密度、不同粒子尺寸的样本检验本文算法，并与以往的梯度结合灰度的方法进行对比。结果表明：本文算法在粒子密度较小的区域能达到92.6%的识别率，在粒子密度较大的区域也能达到86%的识别率，分别比梯度加灰度的方法提高了9.9%和42.7%。解决了粒子重叠的问题，并且对光场和成像效果有更好的鲁棒性。

Abstract

By counting and locating anisotropic conductive film (ACF) particles in the circuit of thin film transistor liquid crystal display (TFT-LCD)

it can determine the circuit's conductivity. In order to solve the overlap problem caused by density increasing of particles in narrow bezel

we put forward a algorithm based on differential interference contrast (DIC) imaging

the algorithm integrates mask method and k-means clustering detection algorithm. After separating particles of bright and shadow

we can effectively segment the particles by judging the entropy of image and the convexity of particles. The value of clustering cluster is discussed

and comparing with the previous method based on gradient and gray level

we test the samples of different particle density and particle size with our proposed algorithm. It indicates that in the case of circuit with the lower particle density

the recognition rate of our method can reach 92.6%

in the area with the higher particle density

the recognition rate can also reach 86%

it is higher than the recognition rate of method combined gradient and gray respectively by 9.9% and 42.7%. The proposed algorithm is also more robust to the light field and the imaging effect.

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Keywords

references

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