采用Gerber文档的印刷电路板表观检测

张静1; 叶玉堂1; 谢煜1; 刘霖1; 常永鑫1

doi:10.3788/OPE.20132110.2679

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采用Gerber文档的印刷电路板表观检测

信息科学 | 更新时间：2020-08-12

- 采用Gerber文档的印刷电路板表观检测
- Appearance detection for printed circuit board by Gerber file
- 光学精密工程 2013年21卷第10期页码：2679-2687
- 作者机构：
  
  电子科技大学光电信息学院，四川成都 610054 2.中国科学院光电技术研究所，四川成都 610209
- 作者简介：
- 基金信息：
  
  波长轮换与相移扫描相结合的表面形貌干涉测量方法();粤港招标关键领域重点突破项目()
- DOI：10.3788/OPE.20132110.2679
  中图分类号： TN41;TP391
- 收稿日期：2013-03-26，
  
  修回日期：2013-05-14，
  
  网络出版日期：2012-10-19，
  
  纸质出版日期：2013-10-15
- 稿件说明：
移动端阅览
张静叶玉堂谢煜刘霖常永鑫. 采用Gerber文档的印刷电路板表观检测[J]. 光学精密工程, 2013,21(10): 2679-2687

ZHANG Jing YE Yu-tang XIE Yu LIU Lin CHANG Yong-xin. Appearance detection for printed circuit board by Gerber file[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2679-2687
张静叶玉堂谢煜刘霖常永鑫. 采用Gerber文档的印刷电路板表观检测[J]. 光学精密工程, 2013,21(10): 2679-2687 DOI： 10.3788/OPE.20132110.2679.

ZHANG Jing YE Yu-tang XIE Yu LIU Lin CHANG Yong-xin. Appearance detection for printed circuit board by Gerber file[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2679-2687 DOI： 10.3788/OPE.20132110.2679.

摘要

对印刷电路板（PCB）进行表观检测时，传统标准板的图像建立是利用PCB图像自身的特征进行配准和分层的，故检测精度不高。本文从PCB表观检测的实际需求出发，提出了新的检测系统。该检测系统引入解析Gerber文档对PCB光电图像进行分层处理，利用形态学的方法自动修正解析后的Gerber文档，建立精确的标准板。根据主分量分析提取彩色图像频带丰富的信息，依据检测缺陷的尺寸大小设置各层模板及检测阈值，实现局部针对性检测，提高检测精度。实验结果表明，与传统的基于颜色分区域方法相比，基于Gerber的方法不仅提高了检测精度，且较大幅度地提高了自动光学检测系统的检测效率，其微小缺陷检测率高达95.1%，25 cm22 cm电路板检测时间仅需1.09 s，满足了在线检测对速度的要求。

Abstract

When the surface defect of a printed-circuit-board (PCB) is detected by a traditional method

it usually has lower detection accuracy for that the traditional method of standard board is built by properties of PCB image itself. According to the accurate requirements of PCB detection

this paper proposes a new detection system. In this system

the layers of PCB photoelectric image were separated by using Gerber file that have been analyzed

then the analyzed Gerber file was corrected by morphology. And a accurate standard board was built. Furthermore

the frequency information in the color image was extracted based on the principal component analysis

and the detection threshold was set according to different information on different layers. By which

the local detection of PCB was completed and detection accuracy was improved. As compared with the traditional method by color stratification

this method improves detection accuracy and detection efficiency of the system. It achieves the detecting probability of 95.1% for micro defects and the detecting time of 1.09 s for a 25 cm22 cm PCB. The detection efficiency of automated optical inspection system can satisfy the requirement of the PCB for defect inspection in online and real time.

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references

MAR N S S, YARLAGADDA P K D V, FOOKES C. Design and development of automatic visual inspection system for PCB manufacturing [J].Robotics and Computer Integrated Manufacturing, 2011, 27(5):949-962.[2]TEOH E K, MITAL D P, LEE B W, et al.. Automated visual inspection of surface mount PCBs [J]. Signal Processing and System Control Factory Automation, 1990, 576-580.[3]WANG D Z, WU C H, ANDREW I, et al.. Fast Multi-template matching using a particle Swarm optimization algorithm for PCB inspection[C]. Conference European Workshops on the Theory and Applications of Evolutionary Computation, 2008, 4974:365-370.[4]SOMATILAKE S, CHALMERS A N. An image based food classification system [C]. Proceedings of Image and Vision Computing, New Zealand, 2007:260-265.[5]EOH E K T, TSENG C C, CHENG M S, et al.. Electric contacts inspection using machine vision [J].Image and Vision Computing, 2010, 28(6):890-901.[6]CHIU S N, PERN M H. Reflection-area-based feature descriptor for solder joint inspection [J].Machine Vision and Applications,2007,8:95-106.[7]刘建伟，梁晋，梁新合，等.大尺寸工业视觉测量系统 [J].光学精密工程，2010,18(1)：126-134.LIU J W, LIANG J, LIANG X H, et al..Industrial vision measuring system for large dimension work-pieces [J].Opt. Precision Eng., 2010, 18(1) ：126-134. (in Chinese)[8]WEI ZH, ZHANG G J, LI X.The application of machine vision in inspecting position-control accuracy of motor control systems [C]. Proceedings of the Fifth International Conference on Electrical Machines and Systems, Shenyang, P.R. China: ICEMS, 2001：1031-1038. [9]姚蛟，叶玉堂，张静,等. PCB自动光学检测中Gerber文件的解析研究 [J]. 计算机工程与设计，2012,33(6):2481-2485.YAO J, YE Y T, ZHANG J, et al.. Research of parsing Gerber file in PCB automatic optical inspection [J]. Computer Engineering and Design, 2012,33(6):2481-2485. (in Chinese)[10]吴福培,邝泳聪,张宪民,等．基于模式匹配及其参数自适应的PCB焊点检测[J].光学精密工程，2009,17(10):2586-2593.WU F P, KUANG Y C, ZHANG X M, et al.. Pattern matching and based PCB solder parameter adaptive joint inspection [J].Opt. Precision Eng., 2009, 17(10):2586-2593.(in Chinese)[11]王映辉.人脸识别原理、方法与技术[M].北京：科学出版社,2010,41-42，124-130，225-240, 142-150.WANG Y H.Recognition Theory,Methods and Techniques[M]. Beijing:Science press, 2010,41-42，124-130，225-240, 142-150. (in Chinese)[12]KISHIMOTO S, KAKIMORI N, YAMAMOTO Y, et al.. A Printed Circuit Board (PCB) inspection system employing the multi-lighting optical system [C]. 8th IEMT International Electronic Manufacturing Technology Conference, IEEE, 1990, 120-129.[13]黄杰贤,李迪,叶峰, 等．挠性印制电路板焊盘表面缺陷的检测[J]. 光学精密工程,2010, 18(11):2443-2553. HUANG J X, LI D, YE F, et al.. Detection of surface defection of solder on flexible printed circuit [J].Opt. Precision Eng., 2010, 18(11):2443-2553.(in Chinese)[14]HAE-WON OH, TAE-HYUNG PARK. Gerber-character recognition system of auto-teaching program for PCB assembly machines [C]. SICE Annual Conference, 2004, 1:300-305.

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