Journal of Intelligent Manufacturing

, Volume 23, Issue 5, pp 1915–1930 | Cite as

Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes

  • Lior RokachEmail author
  • Dan Hutter


A new technique for finding the root cause for problems in a manufacturing process is presented. The new technique is designated to continuously and automatically detect quality drifts on various manufacturing processes and then induce the common root cause. The proposed technique consists of a fast, incremental algorithm that can process extremely high dimensional data and handle more than one root-cause at the same time. Application of such a methodology consists of an on-line machine learning system that investigates and monitors the behavior of manufacturing product routes.


Automatic root cause discovery Data mining Failure analysis Concept drift Quality control Fault detection Yield improvement 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ben-Gal I. (2006) Outlier detection. In: Maimon O., Rokach L. (eds) Data mining and knowledge discovery handbook: A complete guide for practitioners and researchers. Springer, US, New York, pp 131–146Google Scholar
  2. Bergeret F., Le Gall C. (2003) Yield improvement using statistical analysis of process dates. IEEE Transactions on Semiconductor Manufacturing 16: 535–542CrossRefGoogle Scholar
  3. Chang P. C., Fan C. Y., Wang Y. W. (2009) Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory. Journal of Intelligent Manufacturing 20(4): 421–429CrossRefGoogle Scholar
  4. Chen T., Wang Y. C., Wu H. C. (2009) A fuzzy-neural approach for remaining cycle time estimation in a semiconductor manufacturing factory—A simulation study. International Journal of Innovative Computing, Information and Control 5(8): 2125–2140Google Scholar
  5. Choudhary A. K., Harding J. A., Tiwari M. K. (2009) Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5): 501–521CrossRefGoogle Scholar
  6. Duan G., Chen Y. W., Sukekawa T. (2009) Automatic optical inspection of micro drill bit in printed circuit board manufacturing using support vector machines. International Journal of Innovative Computing, Information and Control 5(11(B)): 4347–4356Google Scholar
  7. Durham, J., Marcos, Von J., Vincent, T., Martinez, J., Shelton, S., Fortner, G., Clayton, M. & Felker, S. (1995). Automation and statistical process control of a single wafer etcher in a manufacturing environment. Advanced Semiconductor Manufacturing Conference and Workshop IEEE/SEMI, pp. 213–215.Google Scholar
  8. Frank, E., Hall, M. A., Holmes, G., Kirkby, R., Pfahringer, B., & Witten, I. H. (2005). Weka:Data A machine learning workbench for data mining. In O. Maimon & L. Rokach (Eds.), mining and knowledge discovery handbook: A complete guide for practitioners and researchers (pp. 1305–1314). Springer.Google Scholar
  9. Gardner, M., & Bieker, J. (2000). Data mining solves tough semiconductor manufacturing problems, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 376–383.Google Scholar
  10. Goodwin R., Miller R., Tuv E., Borisov A., Janakiram M., Louchheim S. (2004) Advancements and applications of statistical learning/data mining in semiconductor manufacturing. Intel Technology Journal 8: 325–336Google Scholar
  11. Haapala K. R., Rivera J. L., Sutherland J. W. (2008) Application of life cycle assessment tools to sustainable product design and manufacturing. International Journal of Innovative Computing, Information and Control 4(3): 577–592Google Scholar
  12. Hu, H. C. H., Shun-Feng, S. (2004). Hierarchical clustering methods for semiconductor manufacturing data, Proceeding of the 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 1063–1068.Google Scholar
  13. Hyeon B., Sungshin K., Kwang-Bang W., Gary S., Duk-Kwon L. (2006) Fault detection, diagnosis, and optimization of wafer manufacturing processes utilizing knowledge creation. International Journal of Control, Automation, and Systems 4: 372–381Google Scholar
  14. Jemmy, S., Wynne, H., Mong, L. L. & Tachyang, L. (2005). Mining wafer fabrication: Framework and challenges, next generation of data-mining applications.Google Scholar
  15. Kenneth W., Thomas P., Shaun S. (1999) Using historical wafer data for automated yield analysis. Journal of Vacuum Science Technology A 17: 1369–1376CrossRefGoogle Scholar
  16. Kittler R., Wang W. (1999) The emerging role for data mining. Solid State Technology 42: 45–58Google Scholar
  17. Rodrigues, P., & Gama, J. (2004). Prediction of product quality in continuous glass manufacturing process, 4th European Symposium on Intel Tech and Smart Adaptive Systems, pp. 488–496.Google Scholar
  18. Rokach L. (2010) Ensemble-based classifiers. Artificial Intelligence Review 33(1): 1–39CrossRefGoogle Scholar
  19. Rokach L., Maimon O. (2006) Data mining for improving the quality of manufacturing: a feature set decomposition approach. Journal of Intelligent Manufacturing 17(3): 285–299CrossRefGoogle Scholar
  20. Rokach L., Romano R., Maimon O. (2008) Mining manufacturing databases to discover the effect of operation sequence on the product quality. Journal of Intelligent Manufacturing 19(3): 313–325CrossRefGoogle Scholar
  21. Zengyou H., Xiaofei X., Shengchun D. (2002) Squeezer: An efficient algorithm for clustering categorical data. Journal of Computer Science and Technology 17: 611–624CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Information Systems EngineeringBen-Gurion University of the NegevBeershebaIsrael

Personalised recommendations