Journal of Intelligent Manufacturing

, Volume 25, Issue 5, pp 933–943 | Cite as

Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing

  • Hui-Chun Yu
  • Kuo-Yi Lin
  • Chen-Fu Chien


During semiconductor manufacturing process, massive and various types of interrelated equipment data are automatically collected for fault detection and classification. Indeed, unusual wafer measurements may reflect a wafer defect or a change in equipment conditions. Early detection of equipment condition changes assists the engineer with efficient maintenance. This study aims to develop hierarchical indices for equipment monitoring. For efficiency, only the highest level index is used for real-time monitoring. Once the index decreases, the engineers can use the drilled down indices to identify potential root causes. For validation, the proposed approach was tested in a leading semiconductor foundry in Taiwan. The results have shown that the proposed approach and associated indices can detect equipment condition changes after preventive maintenance efficiently and effectively.


Equipment condition Tool health Preventive maintenance (PM) Fault detection and classification (FDC) Real-time monitoring Manufacturing intelligence Semiconductor manufacturing 



This research was partially supported by National Science Council, Taiwan (NSC100-2628-E-007-017-MY3; NSC102-2622-E-007-013), Taiwan Semiconductor Manufacturing Company (100A0259JC), and National Tsing Hua University under the Toward World-Class University Project (101N2074E1). The authors deeply appreciate constructive comments and suggestions from the anonymous reviewers as well as the supports and inputs of domain experts including Dr. Yi-Chun Chen and Dr. Chun-Ju Wang.


  1. Blue, J., Roussy, A., Thieullen, A., & Pinaton, J. (2012). Efficient FDC based on hierarchical tool condition monitoring scheme. In Proceedings of the Advanced Semiconductor Manufacturing Conference (359–364). New York: Saratoga, Springs, May 2012.Google Scholar
  2. Chao, A., Tseng, S., Wong, D. S., Jane, S., & Lee, S. (2008). Systematic applications of multivariate analysis to monitoring of equipment health in semiconductor manufacturing. In Proceedings of the 2008 Winter Simulation Conference (pp. 2330–2334). Florida: Miami, Dec 2008.Google Scholar
  3. Chen, A., & Blue, J. (2009). Recipe-independent indicator for tool health diagnosis and predictive maintenance. IEEE Transactions on Semiconductor Manufacturing, 22(4), 522–535.CrossRefGoogle Scholar
  4. Chen, A., & Wu, G. (2007). Real-time health prognosis and dynamic preventive maintenance. International Journal of Production Research, 45(15), 3351–3379.CrossRefGoogle Scholar
  5. Chen, W., & Chien, C.-F. (2011). Measuring relative performance of wafer fabrication operations: A case study. Journal of Intelligent Manufacturing, 22(3), 447–457.CrossRefGoogle Scholar
  6. Chien, C.-F., Chen, H., Wu, J., & Hu, C. (2007). Construct the OGE for promoting tool group productivity in semiconductor manufacturing. International Journal of Production Research, 45(3), 509–524.CrossRefGoogle Scholar
  7. Chien, C.-F., Chen, Y., & Peng, J. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496–509.CrossRefGoogle Scholar
  8. Chien, C.-F., & Hsu, C. (2006). A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing. Journal of Intelligent Manufacturing, 17(4), 429–439.CrossRefGoogle Scholar
  9. Chien, C.-F., & Hsu, C. (2011). UNISON Analysis to Model and Reduce Step-and-Scan Overlay Errors for Semiconductor Manufacturing. Journal of Intelligent Manufacturing, 22(3), 399–412.Google Scholar
  10. Chien, C.-F., Hsu, C., & Chang, K. (2013). Overall Wafer Effectiveness (OWE): A Novel Industry Standard for Semiconductor Ecosystem as a Whole. Computers & Industrial Engineering, 65(1), 117–127. Google Scholar
  11. Chien, C.-F., Hsu, C., & Hsiao, C. (2012). Manufacturing intelligence to forecast and reduce semiconductor cycle time. Journal of Intelligent Manufacturing, 23(6), 2281–2294.Google Scholar
  12. Chien, C.-F., & Wu, J. (2003). Analyzing repair decisions in the site imbalance problem of semiconductor test machines. IEEE Transactions on Semiconductor Manufacturing, 16(4), 704–711.Google Scholar
  13. Chien, C.-F., & Zheng, J. (2012). Mini-max regret strategy for robust capacity expansion decisions in semiconductor manufacturing. Journal of Intelligent Manufacturing, 23(6), 2151–2159.CrossRefGoogle Scholar
  14. Cheng, F.-T., Chen, Y., Su, Y., & Zeng, D. (2008). Evaluating reliance level of a virtual metrology system. IEEE Transactions on Semiconductor Manufacturing, 21(1), 92–103.CrossRefGoogle Scholar
  15. Cheng, F.-T., Huang, H., & Kao, C. (2007). Dual-phase virtual metrology scheme. IEEE Transactions on Semiconductor Manufacturing, 20(4), 566–571.Google Scholar
  16. Fisher, R. A. (1925). Statistical Methods for Research Worker. Edinburg and London: Oliver and Boyd.Google Scholar
  17. Hotelling, H. (1947). Multivariate quality control. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques in Statistical Analysis. New York: McGraw-Hill.Google Scholar
  18. Hsu, C.-Y., Chien, C., & Chen, P. (2012). Manufacturing intelligence for early warning of key equipment excursion for advanced equipment control in semiconductor manufacturing. Journal of the Chinese Institute of Industrial Engineers, 29(5), 303–313.CrossRefGoogle Scholar
  19. Liu, R. A. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380–1387.CrossRefGoogle Scholar
  20. Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46–53.CrossRefGoogle Scholar
  21. Montgomery, D. C. (1997). Introduction to statistical quality control (3rd ed.). New York: Wiley.Google Scholar
  22. Ning, X., & Tsung, F. (2012). A density-based statistical process control scheme for high-dimensional and mixed-type observations. IIE Transactions, 44(4), 301–311.CrossRefGoogle Scholar
  23. Rebai, M., Kacem, I., & Adjallah, K. (2012). Earliness-tardiness minimization on a single machine to schedule preventive maintenance tasks: Metaheuristic and exact methods. Journal of Intelligent Manufacturing, 23(4), 1207–1224.Google Scholar
  24. Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), 469–489.CrossRefGoogle Scholar
  25. Wu, J.-Z. (2013). Inventory write-down prediction for semiconductor manufacturing considering inventory age, accounting principle, and product structure with real settings. Computers & Industrial Engineering, 65(1), 128–136.CrossRefGoogle Scholar
  26. Yao, X., Fernadez-Gaucherand, E., Fu, M., & Marcus, S. (2004). Optimal preventive maintenance scheduling in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 17(3), 345–356.CrossRefGoogle Scholar
  27. Yue, H. H., & Tomoyasu, M. (2004). Weighted principal component analysis and its applications to improve FDC performance. In Proceedings of the 43rd IEEE Conference on Decision and Control (pp. 4262–4267). Paradise Island: Bahamas, Dec 2004.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of StatisticsNational Cheng Kung UniversityTainanTaiwan
  2. 2.Department of Industrial Engineering and Engineering ManagementNational Tsing Hua UniversityHsinchuTaiwan

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