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CLC number: TP391; TN45

On-line Access: 2012-11-02

Received: 2012-03-15

Revision Accepted: 2012-08-09

Crosschecked: 2012-09-11

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.11 P.840-849


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

Author(s):  Shao-hu Peng, Hyun Do Nam

Affiliation(s):  Department of Electronics and Electrical Engineering, Dankook University, Yongin 448-701, Korea

Corresponding email(s):   pengshaohu@hotmail.com, hdnam@dankook.ac.kr

Key Words:  Ball grid array (BGA), X-ray, Defect detection, Blob detection, Void detection

Shao-hu Peng, Hyun Do Nam. Void defect detection in ball grid array X-ray images using a new blob filter[J]. Journal of Zhejiang University Science C, 2012, 13(11): 840-849.

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journal="Journal of Zhejiang University Science C",
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%T Void defect detection in ball grid array X-ray images using a new blob filter
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%A Hyun Do Nam
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%DOI 10.1631/jzus.C1200065

T1 - Void defect detection in ball grid array X-ray images using a new blob filter
A1 - Shao-hu Peng
A1 - Hyun Do Nam
J0 - Journal of Zhejiang University Science C
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SP - 840
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%@ 1869-1951
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200065

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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