Skip to main content
SpringerLink
Log in

A data mining approach to the causal analysis of product faults in multi-stage PCB manufacturing

  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

It is difficult for manufacturers of printed circuit boards (PCBs) to remain competitive because of the ever-increasing complexity of circuit board designs and processes that increase the product cost while decreasing the product yield. It is particularly difficult to ensure high yields because the products are made through sequential nano-scale processes, and the quality of each process may be affected by the results of the upstream processes. This type of cumulative effect makes it difficult to determine the machines that introduce product faults. In this paper, we develop a data mining approach to which large amounts of trace data are inputted to infer fault-introducing machines in the form of a L ⇒ R rule, where R contains the fault type and L contains a machine sequence that is the primary cause of the fault type. We tested our approach with industrial lot trace data collected from a PCB manufacturing line with 33 machines and six fault types. From the work-site experiment, we found 26 composite rules that showed a significant cumulative effect. The average fault detection accuracy of the rules was 87.2%. In addition, we found 13 rules that affected more than one fault type.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Shang, Y., Tsung, F., and Zou, C., “Statistical Process Control for Multistage Processes with Binary Outputs,” IIE Transactions, Vol. 45, No. 9, pp. 1008–1023, 2013.

    Article  Google Scholar 

  2. Montgomery, D. C., “Introduction to Statistical Quality Control,” John Wiley & Sons, pp. 288–506, 2009.

    Google Scholar 

  3. Agrawal, R. and Srikant, R., “Fast Algorithms for Mining Association Rules,” Proc. of 20th International Conference on Very Large Data Bases, Vol. 1214, pp. 487–499, 1994.

    Google Scholar 

  4. Dunia, R., Qin, S. J., Edgar, T. F., and McAvoy, T. J., “Identification of Faulty Sensors using Principal Component Analysis,” AIChE Journal, Vol. 42, No. 10, pp. 2797–2812, 1996.

    Article  Google Scholar 

  5. Yan, L., “A PCA-based PCM Data Analyzing Method for Diagnosing Process Failures,” IEEE Transactions on Semiconductor Manufacturing, Vol. 19, No. 4, pp. 404–410, 2006.

    Article  Google Scholar 

  6. Cherry, G. A. and Qin, S. J., “Multiblock Principal Component Analysis based on a Combined Index for Semiconductor Fault Detection and Diagnosis,” IEEE Transactions on Semiconductor Manufacturing, Vol. 19, No. 2, pp. 159–172, 2006.

    Article  Google Scholar 

  7. Ko, J. M., Hong, S. R., Choi, J. Y., and Kim, C. O., “Wafer-to-Wafer Process Fault Detection using Data Stream Mining Techniques,” Int. J. Precis. Eng. Manuf., Vol. 14, No. 1, pp. 103–113, 2013.

    Article  Google Scholar 

  8. Goodlin, B. E., Boning, D. S., Sawin, H. H., and Wise, B. M., “Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools,” Journal of the Electrochemical Society, Vol. 150, No. 12, pp. 778–784, 2003.

    Article  Google Scholar 

  9. Hong, S. J., Lim, W. Y., Cheong, T., and May, G. S., “Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing-Diagnostics,” IEEE Transactions on Semiconductor Manufacturing, Vol. 25, No. 1, pp. 83–93, 2012.

    Article  Google Scholar 

  10. Mahadevan, S. and Shah, S. L., “Fault Detection and Diagnosis in Process Data using One-Class Support Vector Machines,” Journal of Process Control, Vol. 19, No. 10, pp. 1627–1639, 2009.

    Article  Google Scholar 

  11. He, Q. P. and Wang, J., “Fault Detection using the K-Nearest Neighbor Rule for Semiconductor Manufacturing Processes,” IEEE Transactions on Semiconductor Manufacturing, Vol. 20, No. 4, pp. 345–354, 2007.

    Article  Google Scholar 

  12. Chen, F. L. and Liu, S. F., “A Neural-Network Approach to Recognize Defect Spatial Pattern in Semiconductor Fabrication,” IEEE Transactions on Semiconductor Manufacturing, Vol. 13, No. 3, pp. 366–373, 2000.

    Article  Google Scholar 

  13. Jeong, S. H., Choi, D. H., and Jeong, M., “Feasibility Classification of New Design Points using Support Vector Machine Trained by Reduced Dataset,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 5, pp. 739–746, 2012.

    Article  Google Scholar 

  14. Ma, M. D., Wong, D. H., Jang, S. S., and Tseng, S. T., “Fault Detection based on Statistical Multivariate Analysis and Microarray Visualization,” IEEE Transactions on Industrial Informatics, Vol. 6, No. 1, pp. 18–24, 2010.

    Article  Google Scholar 

  15. Chien, C. F., Wang, W. C., and Cheng, J. C., “Data Mining for Yield Enhancement in Semiconductor Manufacturing and an Empirical Study,” Expert Systems with Applications, Vol. 33, No. 1, pp. 192–198, 2007.

    Article  Google Scholar 

  16. Koller, D. and Friedman, N., “Probabilistic Graphical Models: Principles and Techniques,” MIT press, pp. 783–785, 2009.

    Google Scholar 

  17. Yang, L. and Lee, J., “Bayesian Belief Network-based Approach for Diagnostics and Prognostics of Semiconductor Manufacturing Systems,” Robotics and Computer-Integrated Manufacturing, Vol. 28, No. 1, pp. 66–74, 2012.

    Article  MathSciNet  Google Scholar 

  18. Konishi, S. and Kitagawa, G., “Information Criteria and Statistical Modeling,” Springer, pp. 211, 2008.

    MATH  Google Scholar 

  19. Xiong, W., Cao, Y., and Liu, H., “Study of Bayesian Network Structure Learning,” Applied Mathematics and Information Sciences, Vol. 7, No. 1, pp. 49–54, 2013.

    Article  MathSciNet  Google Scholar 

  20. Boulesteix, A. L. and Strimmer, K., “Partial Least Squares: a Versatile Tool for the Analysis of High-Dimensional Genomic Data,” Briefings in Bioinformatics, Vol. 8, No. 1, pp. 32–44, 2007.

    Article  Google Scholar 

  21. Haenlein, M. and Kaplan, A. M., “A Beginner’s Guide to Partial Least Squares Analysis,” Understanding Statistics, Vol. 3, No. 4, pp. 283–297, 2004.

    Article  Google Scholar 

  22. Mehmood, T., Martens, H., Sæbø, S., Warringer, J., and Snipen, L., “A Partial Least Squares based Algorithm for Parsimonious Variable Selection,” Algorithms for Molecular Biology, Vol. 6, No. 1, pp. 27, 2011.

    Article  Google Scholar 

  23. Han, J. and Kamber, M., “Data Mining, Southeast Asia Edition: Concepts and Techniques,” Morgan Kaufmann, pp. 227–248, 2006.

    Google Scholar 

  24. Chong, I. G. and Jun, C. H., “Performance of Some Variable Selection Methods when Multicollinearity is Present,” Chemometrics and Intelligent Laboratory Systems, Vol. 78, No. 1, pp. 103–112, 2005.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Industrial Engineering, Yonsei University, Seoul, 120-749, South Korea

    Hyunsik Sim, Doowon Choi & Chang Ouk Kim

Authors
  1. Hyunsik Sim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Doowon Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chang Ouk Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chang Ouk Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sim, H., Choi, D. & Kim, C.O. A data mining approach to the causal analysis of product faults in multi-stage PCB manufacturing. Int. J. Precis. Eng. Manuf. 15, 1563–1573 (2014). https://doi.org/10.1007/s12541-014-0505-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-014-0505-8

Keywords

Search

Navigation