Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI)
Increasing demand diversity and volume in semiconductor industry (SI) have resulted in shorter product life cycles. This competitive environment, with high-mix low-volume production,
requires sustainable production capacities that can be achieved by reducing unscheduled equipment breakdowns. The fault detection and classification (FDC) is a well-known approach, used in the SI, to improve and stabilize the production capacities. This approach models equipment as a single unit and uses sensors data to identify equipment failures against product and process drifts. Besides its successful deployment for years, recent increase in unscheduled equipment breakdown needs an improved methodology to ensure sustainable capacities. The analysis on equipment utilization, using data collected from a world reputed semiconductor manufacturer, shows that failure durations as well as number of repair actions in each failure have significantly increased. This is an evidence of misdiagnosis in the identification of failures and prediction of its likely causes. In this paper, we propose two lines of defense against unstable and reducing production capacities. First, equipment should be stopped only if it is suspected as a source for product and process drifts whereas second defense line focuses on more accurate identification of failures and detection of associated causes. The objective is to facilitate maintenance engineers for more accurate decisions about failures and repair actions, upon an equipment stoppage. In the proposed methodology, these two lines of defense are modeled as Bayesian network (BN) with unsupervised learning of structure using data collected from the variables (classified as symptoms) across production, process, equipment and maintenance databases. The proofs of concept demonstrate that contextual or statistical information other than FDC sensor signals, used as symptoms, provide reliable information (posterior probabilities) to find the source of product/process quality drifts, a.k.a. failure modes (FM), as well as potential failure and causes. The reliability and learning curves concludes that modeling equipment at module level than equipment offers 45% more accurate diagnosis. The said approach contributes in reducing not only the failure durations but also the number of repair actions that has resulted in recent increase in unstable production capacities and unscheduled equipment breakdowns.
unscheduled maintenance, Bayesian networks, maintenance actions effectiveness, Semiconductor Industry, diagnostic
Blue, J., Roussy, A., Thieullen, A., & Pinaton, J. (2012). Efficient fdc based on hierarchical tool condition monitoring scheme. In Advanced semiconductor manufacturing conference (asmc), 2012 23rd annual semi (pp. 359–364).
Bouaziz, M. F., Zama¨ı, E., & Duvivier, F. (2013). Towards bayesian network methodology for predicting the equipment health factor of complex semiconductor systems. International Journal of Production Research, 51(15), 4597–4617.
Chang, H. J., Song, D. S., Kim, P. J., & Choi, J. Y. (2012). Spatiotemporal pattern modeling for fault detection and classification in semiconductor manufacturing. Semiconductor Manufacturing, IEEE Transactions on, 25(1), 72–82.
Chen, A., & Blue, J. (2009). Recipe-independent indicator for tool health diagnosis and predictive maintenance. Semiconductor Manufacturing, IEEE Transactions on, 22(4), 522–535.
Chickering, D. M. (1996). Learning bayesian networks is np-complete. In Learning from data (pp. 121–130). Springer.
Chickering, D. M. (2002). Learning equivalence classes of bayesian-network structures. The Journal of Machine Learning Research, 2, 445–498.
Correa, M., Bielza, C., & Pamies-Teixeira, J. (2009). Comparison of bayesian networks and artificial neural networks for quality detection in a machining process. Expert Systems with Applications, 36(3), 7270–7279.
de Souza e Silva, E., & Ochoa, P. M. (1992). State space exploration in markov models. In Acm sigmetrics performance evaluation review (Vol. 20, pp. 152–166).
Doty, L. A. (1996). Statistical process control. Industrial Press Inc.
Geisser, S. (1993). Predictive inference (Vol. 55). CRC Press.
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & operations research, 13(5), 533–549.
He, Q. P., & Wang, J. (2007). Fault detection using the knearest neighbor rule for semiconductor manufacturing processes. Semiconductor manufacturing, IEEE transactions on, 20(4), 345–354.
Isham, M. U. (2013). Real time safety verification in the process industry using fault semantic networks (fsn) (Unpublished doctoral dissertation). University of Ontario Institute of Technology.
Ishikawa, K. (1990). Introduction to quality control. Productivity Press.
Kjærulff, U. B., & Madsen, A. L. (2006). Probabilistic networks for practitionersa guide to construction and analysis of bayesian networks and influence diagrams. Department of Computer Science, Aalborg University, HUGIN Expert A/S.
Kobbacy, K. A., Vadera, S., McNaught, K., & Chan, A. (2011). Bayesian networks in manufacturing. Journal of Manufacturing Technology Management, 22(6), 734–747.
Kumar, R. (2008). Fabless semiconductor implementation. McGraw-Hill New York.
Lacaille, J., & Zagrebnov, M. (2007). An unsupervised diagnosis for process tool fault detection: the flexible golden pattern. Semiconductor Manufacturing, IEEE Transactions on, 20(4), 355–363.
Lam,W., & Bacchus, F. (1994). Learning bayesian belief networks: An approach based on the mdl principle. Computational intelligence, 10(3), 269–293.
Margaritis, D. (2003). Learning bayesian network model structure from data (Unpublished doctoral dissertation). US Army.
Munteanu, P., & Bendou, M. (2001). The eq framework for learning equivalence classes of bayesian networks. In Data mining, 2001. icdm 2001, proceedings ieee international conference on (pp. 417–424).
Pearl, J. (2014). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann.
Roeder, G., Mattes, A., Pfeffer, M., Schellenberger, M., Pfitzner, L., Knapp, A., . . . others (2012). Framework for integration of virtual metrology and predictive maintenance. In Advanced semiconductor manufacturing conference (asmc), 2012 23rd annual semi (pp. 288–293).
Rooney, J. J., & Heuvel, L. N. V. (2004). Root cause analysis for beginners. Quality progress, 37(7), 45–56.
Sarkar, P. (2004). Clustering of event sequences for failure root cause analysis. Quality engineering, 16(3), 451–460.
Shahzad, M. K. (2012). Exploitation dynamique des donnes de production pour amliorer les mthodes dfm dans lindustry microlectronique (Unpublished doctoral dissertation). GSCOP Laboratory.
Shahzad, M. K., Hubac, S., Siadat, A., & Tollenaere, M. (2011). An extended idm business model to ensure time-to-quality in semiconductor manufacturing industry.In Enterprise information systems (pp. 118–128). Springer.
Smith, G. M. (1998). Statistical process control and quality improvement. Prentice Hall.
Teyssier, M., & Koller, D. (2012). Ordering-based search: A simple and effective algorithm for learning bayesian networks. arXiv preprint arXiv:1207.1429.
Verron, S., Li, J., & Tiplica, T. (2010). Fault detection and isolation of faults in a multivariate process with bayesian network. Journal of Process Control, 20(8), 902–911.
Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671–682.
Weidl, G., Madsen, A., & Israelson, S. (2005). Applications of object-oriented bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes. Computers & chemical engineering, 29(9), 1996–2009.
Yang, L., & Lee, J. (2012). Bayesian belief networkbased approach for diagnostics and prognostics of semiconductor manufacturing systems. Robotics and Computer-Integrated Manufacturing, 28(1), 66–74.
Yoon, M., & Malerba, F. (2010). Technological interrelatedness, knowledge generality and economies of scale in the evolution of firm boundaries: A history-friendly model of the fabless ecosystem. In International conference on opening up innovation: Strategy, organization and technology.
Yue, H. H., & Tomoyasu, M. (2004). Weighted principal component analysis and its applications to improve fdc performance. In Decision and control, 2004. cdc. 43rd ieee conference on (Vol. 4, pp. 4262–4267).