Skip to main content
Log in

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

  • Published:
Journal of Zhejiang University SCIENCE C Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bay, H., Ess, A., Tuytelaars, T., Gool, L.V., 2008. Speeded-up robust features (SURF). Comput. Vis. Image Understand., 110(3):346–359. [doi:10.1016/j.cviu.2007.09.014]

    Article  Google Scholar 

  • Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679–698. [doi:10.1109/TPAMI.1986.4767851]

    Article  Google Scholar 

  • Lin, W.C., 2007. The Void-Free Reflow Soldering of BGA with Vacuum. 8th Int. Conf. on Electronic Packaging Technology, p.1–5. [doi:10.1109/ICEPT.2007.4441462]

  • Liu, Y.M., Ye, L.B., Zheng, P.Y., Shi, X.R., Hu, B., Liang, J., 2010. Multiscale classification and its application to process monitoring. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 11(6):425–434. [doi:10.1631/jzus.C0910430]

    Article  MATH  Google Scholar 

  • Maini, R., Aggarwal, H., 2009. Study and comparison of various image edge detection techniques. Int. J. Image Process., 3(1):1–11.

    Article  Google Scholar 

  • Otsu, N., 1979. A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern., 9(1):62–66. [doi:10.1109/TSMC.1979.4310076]

    Article  MathSciNet  Google Scholar 

  • Peng, S.H., Muzzammil, K., Kim, D.H., 2010a. Robust Feature Detection Based on Local Variation for Image Retrieval. 17th IEEE Int. Conf. on Image Processing, p.1033–1036. [doi:10.1109/ICIP.2010.5652973]

  • Peng, S.H., Kim, D.H., Lee, S.L., Lim, M.K., 2010b. Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images. Comput. Biol. Med., 40(11–12):931–942. [doi:10. 1016/j.compbiomed.2010.10.005]

    Article  Google Scholar 

  • Rooks, S.M., Benhabib, B., Smith, K.C., 1995. Development of an inspection process for ball-grid-array technology using scanned-beam X-ray laminography. IEEE Trans. Comp. Pack. Manuf. Technol. Part A, 18(4):851–861. [doi:10.1109/95.477473]

    Article  Google Scholar 

  • Said, A.F., Bennett, B.L., Karam, L.J., Pettinato, J., 2010. Robust Automatic Void Detection in Solder Balls. IEEE Int. Conf. on Acoustics Speech and Signal Processing, p.1650–1653. [doi:10.1109/ICASSP.2010.5495524]

  • Sa-nguannam, A., Srinonchat, J., 2008. Analysis Ball Grid Array Defects by Using New Image Technique. 9th Int. Conf. on Signal Processing, p.785–788. [doi:10.1109/ ICOSP.2008.4697247]

  • Sankaran, V., Kalukin, A.R., Kraft, R.P., 1998. Improvements to X-ray laminography for automated inspection of solder joints. IEEE Trans. Comp. Pack. Manuf. Technol. Part C, 21(2):148–154. [doi:10.1109/3476.681394]

    Article  Google Scholar 

  • Seul, M., O’Gorman, L., Sammon, M.J., 2008. Practical Algorithms for Image Analysis: Descriptions, Examples, and Code. Cambridge University Press, UK.

    Google Scholar 

  • Sumimoto, T., Maruyama, T., Azuma, Y., Goto, S., Mondo, M., Furukawa, N., Okada, S., 2002. Detection of Defects at BGA Solder Joints by Using X-Ray Imaging. IEEE Int. Conf. on Industrial Technology, p.238–241. [doi:10.1109/ ICIT.2002.1189898]

  • Teramoto, A., Murakoshi, T., Tsuzaka, M., Fujita, H., 2007. Automated solder inspection technique for BGA-mounted substrates by means of oblique computed tomography. IEEE Trans. Electr. Pack. Manuf., 30(4):285–292. [doi:10.1109/TEPM.2007.907574]

    Article  Google Scholar 

  • Viola, P., Jones, M., 2001. Rapid Object Detection Using a Boost Cascade of Simple Features. Int. Conf. on Computer Vision and Pattern Recognition, 1:511–518. [doi:10. 1109/CVPR.2001.990517]

    Google Scholar 

  • Xia, N.J., Cao, Q.X., Fu, Z., Lee, J., 2004. A Machine Vision System of Ball Grid Array Inspection on RT-Linux OS. Int. Conf. on the Business of Electronic Product Reliability and Liability, p.81–85. [doi:10.1109/BEPRL.2004.1308154]

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyun Do Nam.

Additional information

Project supported by the Dankook University 2010 Funding for Research Institute of Information and Communication Convergence Technology (RICT), Korea

Rights and permissions

Reprints and permissions

About this article

Cite this article

Peng, Sh., Nam, H.D. Void defect detection in ball grid array X-ray images using a new blob filter. J. Zhejiang Univ. - Sci. C 13, 840–849 (2012). https://doi.org/10.1631/jzus.C1200065

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.C1200065

Key words

CLC number