AOI BASED NEUROFUZZY SYSTEM TO EVALUATE SOLDER JOINT QUALITY

G. Acciani, G. Brunetti, G. Fornarelli, A. Giaquinto, D. Maiullari

2009

Abstract

Surface Mount Technology is extensively used in the production of Printed Circuit Boards due to the high level of density in the electronic device integration. In such production process several defects could occur on the final electronic components, compromising their correct working. In this paper a neurofuzzy solution to process information deriving from an automatic optical system is proposed. The designed solution provides a Quality Index of a solder joint, by reproducing the modus operandi of an expert and making it automatic. Moreover, the considered solution presents some attractive advantages: a complex acquisition system is not needed, reducing the equipment costs and shifting the assessment of a solder joint on the fuzzy parts. Finally, the typical low computational costs of the fuzzy systems could satisfy urgent time constrains in the in-line detection of some industrial productive processes.

References

  1. Krippner, P., and Beer, D., 2004. “AOI Testing Positions in Comparision”, Circuit Assembly, Apr. 2004, pp. 26- 32.
  2. Manjeshwar, P., Craik, J., Phadnis, S., and Srihari, K., 2006. 'Effectiveness Study of an Automated 3D Laminography X-Ray Inspection System in a High Volume-Low-Mix SMT line', The Int. J. of Adv. Manuf. Technol., vol. 30 (11-12), pp. 1191 - 1201.
  3. Teramoto, A., Murakoshi, T., Tsuzaka, M., and Fujita, H., 2007. “Automated Solder Inspection Technique for BGA-Mounted Substrates by Means of Oblique Computed Tomography”, IEEE Trans. on Electronics Packaging Manufacturing, vol. 30 (4) , pp. 285 - 292.
  4. Wu, Y. P., Tu, P. L., and Chan, Y. C., 2001. “The effect of solder paste volume and reflow ambient atmosphere on reliability of CBGA assemblies,” J. Electron. Packag., vol. 123 (3), pp. 284-289.
  5. Zhang, L., Ume, I.C., Gamalski, J., and Galuschki K. P., 2006. “Detection of Flip Chip Solder Joint Cracks Using Correlation Coefficient and Auto-Comparison Analyses of Laser Ultrasound Signals”, IEEE Trans. on Comp. and Pack. Technol., vol. 29 (1), pp 13-19.
  6. Hsu-Nan, Y., Du-Ming, T., and Jun-Yi Y., 2006. “FullField 3-D Measurement of Solder Pastes Using LCDBased Phase Shifting Techniques”, IEEE Trans. on Electron. Packag. Manuf., vol. 29 (1), pp. 50 - 57.
  7. Ko, K.W., and Cho H.S., 2000. “Solder joint inspection using a neural network and fuzzy rule-based classification method”, IEEE Trans. on Electron. Packag. Manuf., vol. 23 (2), pp. 93-103.
  8. Jagannathan, S., 1997. “Automatic Inspection of Wave Soldered Joints Using Neural Networks”, J. of Manuf. Syst., vol. 16 (6), pp. 389-398.
  9. Acciani, G., Brunetti, G., and Fornarelli G., 2006. “A Multiple Neural Network System to Classify Solder Joints of Integrated Circuits”, Int. J. of Computational Intelligence Research., vol. 2 (4), pp. 337-348.
Download


Paper Citation


in Harvard Style

Acciani G., Brunetti G., Fornarelli G., Giaquinto A. and Maiullari D. (2009). AOI BASED NEUROFUZZY SYSTEM TO EVALUATE SOLDER JOINT QUALITY . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 51-54. DOI: 10.5220/0001859300510054


in Bibtex Style

@conference{iceis09,
author={G. Acciani and G. Brunetti and G. Fornarelli and A. Giaquinto and D. Maiullari},
title={AOI BASED NEUROFUZZY SYSTEM TO EVALUATE SOLDER JOINT QUALITY},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={51-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001859300510054},
isbn={978-989-8111-85-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AOI BASED NEUROFUZZY SYSTEM TO EVALUATE SOLDER JOINT QUALITY
SN - 978-989-8111-85-2
AU - Acciani G.
AU - Brunetti G.
AU - Fornarelli G.
AU - Giaquinto A.
AU - Maiullari D.
PY - 2009
SP - 51
EP - 54
DO - 10.5220/0001859300510054