Multimedia Tools and Applications

, Volume 78, Issue 6, pp 7871–7892 | Cite as

Detection of oxidation region of flexible integrated circuit substrate based on topology mapping

  • Zhiyan Zhong
  • Yueming HuEmail author


Vision inspection has been extensively used in the field of defect measurement field as a non-contact and nondestructive measurement technique. The conventional methods of visual defect detection rely heavily on the standard templates and features of image, but the standard templates are difficult to obtain or even don’t exist in many application fields especially for the field of flexible Integrated Circuit (IC) substrate defect detection based on micro vision. To solve the above problems, an algorithm of oxidation defects detection based on topology mapping. First, the filter template of weighted average neighborhood closed curve based on topology mapping is used to remove the image noise. Second, an image segmentation method is proposed by using the idea of maximum variance between classes to find out the threshold, then the spatially filtered is used to remove the normal surface texture and the structure of the substrate which is introduced by metallographic microscope enlargement. Finally, experiments are performed to show that the filter template of weighted average neighborhood closed curve is better than the existing filters and effectively removes image noise, then the segmentation method in this paper works better.


Flexible integrated circuit packaging substrates Topology mapping Defect inspection Image segmentation 



This study was funded by the National Natural Science Foundation of China (Grant No.61573146), the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No.2014ZX 02503), the Applied Science and Technology Research and Development Special Fund Project of Guangdong Province, China (Grant No.2015B010133003), the Natural Science Foundation of Guangdong Province, China (Grant No.2016A030313454).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.Engineering Research Center for Precision Electronic Manufacturing Equipments of Ministry of EducationGuangzhouPeople’s Republic of China

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