Abstract
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.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
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 131–146
Bergeret F., Le Gall C. (2003) Yield improvement using statistical analysis of process dates. IEEE Transactions on Semiconductor Manufacturing 16: 535–542
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): 421–429
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): 2125–2140
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): 501–521
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)): 4347–4356
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.
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.
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.
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: 325–336
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): 577–592
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.
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: 372–381
Jemmy, S., Wynne, H., Mong, L. L. & Tachyang, L. (2005). Mining wafer fabrication: Framework and challenges, next generation of data-mining applications.
Kenneth W., Thomas P., Shaun S. (1999) Using historical wafer data for automated yield analysis. Journal of Vacuum Science Technology A 17: 1369–1376
Kittler R., Wang W. (1999) The emerging role for data mining. Solid State Technology 42: 45–58
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.
Rokach L. (2010) Ensemble-based classifiers. Artificial Intelligence Review 33(1): 1–39
Rokach L., Maimon O. (2006) Data mining for improving the quality of manufacturing: a feature set decomposition approach. Journal of Intelligent Manufacturing 17(3): 285–299
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): 313–325
Zengyou H., Xiaofei X., Shengchun D. (2002) Squeezer: An efficient algorithm for clustering categorical data. Journal of Computer Science and Technology 17: 611–624
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rokach, L., Hutter, D. Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes. J Intell Manuf 23, 1915–1930 (2012). https://doi.org/10.1007/s10845-011-0517-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-011-0517-5