Methodology for Integrated Failure-Cause Diagnosis with Bayesian Approach: Application to Semiconductor Manufacturing Equipment

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Asma ABU SAMAH Muhammad Kashif SHAHZAD Eric ZAMAÏ Stéphane HUBAC

Abstract

Semiconductor Industry (SI) is facing the challenge of short product life cycles due to increasing diversity in customer demands. As a result, it has transformed into a high-mix low -volume production line that requires sustainable production capacities. However, significant increase in the unscheduled equipment breakdowns, limits its success. It is observed that in a high-mix low-volume production, product commonality is inversely proportional to failure occurrences and number of corrective actions in each failure. This provides evidence of misdiagnosis for equipment failures and causes. Moreover, equipment is believed to be the only source for product quality drifts that increase the unscheduled breakdowns and result in unstable production capacities. In this paper, we propose two defense lines against increasing unscheduled equipment breakdowns due to misdiagnosis. We argue that product quality drift can be traced to product itself, process and maintenance events, besides equipment. The Bayesian Belief Network (BBN) is proposed using symptoms, collected across drift sources, that improves equipment breakdown decisions by accurately identifying the source of product quality drift. The misdiagnosis of equipment failures and causes, if equipment is found as a source of drift, is another significant factor for increasing unscheduled equipment breakdowns. Existing failures and causes diagnosis approaches, in the SI, model equipment as a single unit and use fault detection and classification (FDC) sensor data. We also argue that these are the key reasons for the misdiagnosis because of neglected facts that production equipment is composed of multiple modules and FDC sensors undergo reliability issues in a high-mix low-volume production line. Therefore, to improve these misdiagnosis, another BBN is proposed that uses statistical information, collected from the equipment database, at the module level. These BBN models are evaluated in a thermal treatment (TT) workshop at the world reputed semiconductor manufacturer. The BBN model for the identification of the source of product quality drift (failure mode) demonstrates 97.8% prediction accuracy; whereas, module level BBNs for equipment failures and causes diagnosis are found 45.7% more accurate than equipment level BBN.

How to Cite

ABU SAMAH, A., SHAHZAD, M. K., ZAMAÏ, E., & HUBAC, S. (2014). Methodology for Integrated Failure-Cause Diagnosis with Bayesian Approach: Application to Semiconductor Manufacturing Equipment. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1548
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Keywords

Failure-cause diagnosis, Bayesian Belief Network, Semiconductor Industry, Product Quality Drift Diagnosis

References
Ballhaus, W., Pagella, A., & Vogel, C. (2009). A change of pace for the semiconductor industry. Technical report, German Technology, Media and Telecommunications, Nov. 2009.
Blue, J., Roussy, A., Thieullen, A., & Pinaton, J. (2012). Efficient FDC based on hierarchical tool condition monitoring scheme. 23rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) (pp. 359-364), May 2012. Bouaziz, M. F., Zamaï, E., & Duvivier, F. (2013). Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems. International Journal of Production Research (IJPR), 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. IEEE Transactions on Semiconductor Manufacturing, 25(1), 72-82.
Chen, A., & Blue J. (2009). Recipe-independent indicator for tool health diagnosis and predictive maintenance. IEEE Transactions on Semiconductor Manufacturing, 22(4), 522-535.
Chickering, D.M. (2002). Learning equivalence classes of Bayesian-network structures. The Journal of Machine Learning Research, 2, 445-498. Demirli, K., & Vijayakumar, S. (2010). Fuzzy logic based assignable cause diagnosis using control chart patterns. Information Sciences, 180(17), 3258-3272.
Doty, L. A. (1996). Statistical process control (2nd ed.). NY, USA: Industrial Press. Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13(5), 533-549.
He, Q.P., & Wang J. (2007). Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 20(4), 345 ± 354.
Isham, M.U. (2013). Real time safety verification in the process industry using fault semantic networks (FSN). Doctoral dissertation. Institute of technology, University of Ontario. Ishikawa, K., & Loftus, J. H. (1990). Introduction to quality control (Vol. 98). Tokyo: 3A Corporation.
Kjærulff, U.B., & Madsen A.L. (2006). Probabilistic Networks for Practitioners – A Guide to Construction and Analysis of Bayesian Networks and Influence Diagrams, Department of Computer Science, Aalborg University, HUGIN Expert A/S. Kumar, R. (2008). Fabless semiconductor implementation. New York: McGraw-Hill.
Lacaille, J.,& Zagrebnov, M. (2007). An unsupervised diagnosis for process tool fault detection: The flexible golden pattern. IEEE Transactions on Semiconductor Manufacturing, 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.Doctoral dissertation. University of Pittsburgh. Munteanu, P., & Bendou, M. (2001). The EQ framework for learning equivalence classes of Bayesian networks. Proceeding IEEE International Conference on Data Mining (ICDM), (417-424). Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann.
Roeder, G., Schellenberger, M., Schoepka, U., Pfeffer, M., Winzer ,S., Jank, S., & Pfitzner L. (2011). Approaches for Implementation of Virtual Metrology and Predictive Maintenance into Existing Fab Systems. Retrieved from http://convegni.unicatt.it/miproco/meetings_Roeder2011_06_13_Statistical_Workshop_Milan_final.pdf on 3rd January 2014. 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-q460. Selen, L. J. M., Timmermans, E. A. H., & Bolscher, G. (2009). U.S. Patent No. 7553516 B2. Washington, DC: U.S. Patent and Trademark Office. Retrieved from http://www.google.fr/ patents/US7553516 on 21st March 2014
Shahzad, M. K., Hubac, S., Siadat, A., & Tollenaere, M. (2011). An Extended Business Model to Ensure Time-to-Quality in Semiconductor Manufacturing Industry. International Conference on Enterprise Information Systems, Portugal.
Smith, G. M. (2004). Statistical process control and quality improvement (5th ed.). NJ, USA: Pearson/Prentice Hall.
Susto, G., Pampuri, S., Schirru, A., & Beghi, A. (2012). Optimal tuning of epitaxy pyrometers. Proceeding of 23rd IEEE/SEMI Advanced Semiconductor Manufacturing Conference (ASMC), (294–299).
Teyssier, M. & Koller, D. (2005). Ordering-based search: A simple and effective algorithm for learning Bayesian networks. Conference in Uncertainty of Artificial Intelligence (UAI).
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., and Jouffe, L., (2006), Complex system reliability modeling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliability Engineering and system Safety, 91, 149-162. Weidl, G., Madsen, A. L., & 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 Network-based approach for diagnostics and prognostics of semiconductor manufacturing. Robotics and Computer-Integrated Manufacturing, 28, 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. International Conference on Opening Up Innovation: Strategy, Organization and Technology.
Yue, H.H. & Tomoyasu, M. (2004). Weighted PCA and its applications to improve FDC performance. Proceeding 43rd IEEE Conference on Decision and Control, (4262–4267).
Section
Technical Papers