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

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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.

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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

    Google Scholar 

  • Bergeret F., Le Gall C. (2003) Yield improvement using statistical analysis of process dates. IEEE Transactions on Semiconductor Manufacturing 16: 535–542

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Kittler R., Wang W. (1999) The emerging role for data mining. Solid State Technology 42: 45–58

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Zengyou H., Xiaofei X., Shengchun D. (2002) Squeezer: An efficient algorithm for clustering categorical data. Journal of Computer Science and Technology 17: 611–624

    Article  Google Scholar 

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Correspondence to Lior Rokach.

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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

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  • DOI: https://doi.org/10.1007/s10845-011-0517-5

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