Void defect detection in ball grid array X-ray images using a new blob filter

Shao-hu PENG; Hyun Do NAM

doi:10.1631/jzus.C1200065

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Void defect detection in ball grid array X-ray images using a new blob filter

Computer & Automation | Updated：2022-12-09

- Void defect detection in ball grid array X-ray images using a new blob filter
- Void defect detection in ball grid array X-ray images using a new blob filter
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 11, Pages: 840-849(2012)
- Affiliations：
  
  Department of Electronics and Electrical Engineering, Dankook University, Yongin 448-701, Korea
- Author bio：
  
  Hyun Do NAM, E-mail:hdnam@dankook.ac.kr
- Funds：
- DOI：10.1631/jzus.C1200065
  CLC： TP391;TN45
- Received：15 March 2012，
  
  Revised：2012-;9-11，
  
  Published：2012-11
- Accepted：
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Shao-hu PENG, Hyun Do NAM. Void defect detection in ball grid array X-ray images using a new blob filter[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(11): 840-849.
DOI：

Shao-hu PENG, Hyun Do NAM. Void defect detection in ball grid array X-ray images using a new blob filter[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(11): 840-849. DOI： 10.1631/jzus.C1200065.

摘要

Abstract

Ball grid arrays (BGAs) have been used in the production of electronic devices/assemblies because of their advantages of small size

high I/O port density

etc. However

BGA voids can degrade the performance of the board and cause failure. In this paper

a novel blob filter is proposed to automatically detect BGA voids presented in X-ray images. The proposed blob filter uses the local image gradient magnitude and thus is not influenced by image brightness

void position

or component interference. Different sized average box filters are employed to analyze the image in multi-scale

and as a result

the proposed blob filter is robust to void size. Experimental results show that the proposed method obtains void detection accuracy of up to 93.47% while maintaining a low false ratio. It outperforms another recent algorithm based on edge detection by 40.69% with respect to the average detection accuracy

and by 16.91% with respect to the average false ratio.

关键词

Keywords

references

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