Skip to main content
Log in

Locating and checking of BGA pins’ position using gray level

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

A machine vision system for SMT-mounting machine applications usually involves a two-stage algorithm. It first measures the centroid and rotation angle of the SMD, and then checks each pin’s area, position error, and grid coordinate. In this paper a set of complete procedures is proposed to locate and check the BGA image. During the locating procedures, one first calculates a threshold for the frame using an iterative threshold algorithm. If an object is found under this threshold, then the pin area is calculated by a local threshold. After that, whether this object is a pin or not is decided by its neighbouring pins’ relative positions, then the approximate rotation angle for finding the outer pins is calculated, and the centroid as well as the rotation angle of a BGA component is calculated by the rectangular least-squares algorithm. The checking procedure also measures each pin’s area using the moment algorithm, it then calculates the radius of the moving sum using each pin’s area, and finally measures the position error using a moving-sum algorithm and judges each pin’s type by gray level. The new method uses the gray level statistic information to solve the empty pad problem and utilizes the symmetrical property of a circle to deal with the shape problem. Lastly, the cyclic redundancy check (CRC) algorithm is used to check the correspondence between each pin and its pin type. This new method has a high accuracy and reduced execution time and meets the crucial time requirement of a high-speed SMT machine through experimental verification.

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

Access this article

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

  1. Ayoub GT (1990) Machine vision in high-accuracy SMT autoplacers. Circuits Manuf 30(1):28–32

    Google Scholar 

  2. Woolstenhulme J, Lubofsky E (2000) Machine vision placement considerations. Surf Mount Technol 14(11):62–66

    Google Scholar 

  3. Capson DW, Tsang MC (1990) An experimental vision system for SMD component placement inspection. IEEE Industrial Electronics Society 16th Annual Conference, Vol. 1, pp 815–820

  4. Yeh CH, Tsai DM (2001) A rotation-invariant and non-referential approach for ball grid array (BGA) substrate conducting path inspection. Int J Adv Manuf Technol 17(6):412–424

    Article  Google Scholar 

  5. Hara Y, Akiyama N, Karasaki K (1983) Automatic inspection system for printed circuit boards. IEEE Trans Pattern Anal Mach Intell 5(6):623–630

    Google Scholar 

  6. West GA (1984) A system for the automatic visual inspection of bare-printed circuit boards. IEEE Trans Syst Man Cybern 14(5):767–773

    Google Scholar 

  7. Benhabib B, Charette CR, Smith KC, Yip AM (1990) Automatic visual inspection of printed circuit boards: an experimental system. Int J Robotics Autom 5(2):49–58

    Google Scholar 

  8. Hata S, Hagimae K, Hibi S, Gunji T (1989) Assembled PCB visual inspection machine using image processor with DSP. IECON’89 15th Annual Conference of IEEE Industrial Electronics, Vol. 3, pp 572–577

  9. Baartman JP, Brennemann AE, Buckley SJ, Moed MC (1990) Placing surface mount components using coarse/fine positioning and vision. IEEE Trans Components Hybrids Manuf Technol 13(3):559–564

    Article  Google Scholar 

  10. Burel G, Bernard F, Venema WJ (1995) Vision feedback for SMD placement using neural networks. Proceedings of 1995 IEEE International Conference on Robotics and Automation, Vol. 2, pp 1491–1496

  11. Belkasim S, Ghazal A, Basir O (2000) Edge enhanced optimun automatic thresholding. Proceedings of 2000 ICS: Workshop on Image Processing and Pattern Recognition, pp 78–85

    Google Scholar 

  12. Kittler J, Illingworth J (1984) An automatic threholding algorithm and its performance. In: Proc. Seventh Int. Conference, Pattern Recognition, Vol. 1, pp 287–289

  13. Tsai WH (1985) Moment-preserving thresholding: a new approach. Comput Vis Graph Image Process 29(3):377–393

    Google Scholar 

  14. Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 8(8):630–632

    Google Scholar 

  15. Shaoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41(2):233–260

    Article  Google Scholar 

  16. Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill, NY

  17. Sheu HT, Wen SH (2002) A high end SMD inspection system using template-based mask. 15-th IPPR Conference on Computer Vision, Graphics and Image Processing, August, pp 306–310

  18. Maze JE, Saltzberg BR (1991) Error-burst detection with random CRCs. IEEE Trans Commun 39(8):1175–1178

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ching-Long Shih.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shih, CL., Ruo, CW. & Sheu, HT. Locating and checking of BGA pins’ position using gray level. Int J Adv Manuf Technol 26, 491–498 (2005). https://doi.org/10.1007/s00170-003-1617-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-003-1617-y

Keywords

Navigation