Confidence estimation of feedback information for logicdiagnosis

This paper proposes an estimation method for the confidence level of feedback information (CLFI), namely the confidence level of reported information in computer integrated manufacturing (CIM) architecture for logic diagnosis. This confidence estimation provides a diagnosis module with precise reported information to quickly identify the origin of equipment failure. We studied the factors affecting CLFI, such as measurement system reliability, production context, position of sensors in the acquisition chains, type of products, reference metrology, preventive maintenance and corrective maintenance based on historical data and feedback information generated by production equipments. We introduced the new 'CLFI' concept based on the Dynamic Bayesian Network approach and Tree Augmented Naive Bayes model. Our contribution includes an on-line confidence computation module for production equipment data, and an algorithm to compute CLFI. We suggest it to be applied to the semiconductor manufacturing industry.

[1]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[2]  Pedro M. Domingos,et al.  Naive Bayes models for probability estimation , 2005, ICML.

[3]  C. Seatzu,et al.  Fault diagnosis and identification of discrete event systems using Petri nets , 2008, 2008 9th International Workshop on Discrete Event Systems.

[4]  Balbir S. Dhillon.  Dynamic Reliability Evaluation Models , 2004 .

[5]  Daniel Shevitz,et al.  Relating confidence to measured information uncertainty in qualitative reasoning , 2011, 2011 Annual Meeting of the North American Fuzzy Information Processing Society.

[6]  M. Combacau,et al.  An architecture for control and monitoring of discrete events systems , 1998 .

[7]  Antonia J. Jones,et al.  A multi-layer/multi-level control architecture for computer integrated manufacturing systems , 1989, 15th Annual Conference of IEEE Industrial Electronics Society.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Sébastien Henry,et al.  Logic control law design for automated manufacturing systems , 2012, Eng. Appl. Artif. Intell..

[10]  H. H. Avilés-Arriaga,et al.  A Comparison of Dynamic Naive Bayesian Classifiers and Hidden , 2011 .

[11]  Mohammed Farouk Bouaziz,et al.  Dependability of complex semiconductor systems: Learning Bayesian networks for decision support , 2011, 2011 3rd International Workshop on Dependable Control of Discrete Systems.

[12]  Juan Zhou,et al.  Learning effective brain connectivity with dynamic Bayesian networks , 2007, NeuroImage.

[13]  Éric Zamaï,et al.  DIAGNOSIS FOR CONTROL SYSTEM RECONFIGURATION , 2007 .

[14]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[15]  Belgacem Bettayeb,et al.  Quality and exposure control in semiconductor manufacturing. Part I: Modelling , 2012 .

[16]  Christoforos N. Hadjicostis,et al.  Fault Diagnosis in Discrete Event Systems Modeled by Partially Observed Petri Nets , 2009, Discret. Event Dyn. Syst..

[17]  Keiji Kanazawa,et al.  A model for reasoning about persistence and causation , 1989 .

[18]  Hamdi A. Awad,et al.  Fault-diagnosis in discrete event systems: Improvements and new results , 2011 .

[19]  Stéphane Lafortune,et al.  Failure diagnosis of dynamic systems: an approach based on discrete event systems , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[20]  John Scanlan.  Fault Detection and Classification , 2004 .

[21]  Philippe Weber,et al.  Bayesian networks implementation of the Dempster Shafer theory to model reliability uncertainty , 2006, First International Conference on Availability, Reliability and Security (ARES'06).

[22]  Cristina E. Manfredotti,et al.  Modeling and Inference with Relational Dynamic Bayesian Networks , 2009, Canadian AI.

[23]  Yannick Pencolé,et al.  Monitoring and Active Diagnosis for Discrete-Event Systems , 2009 .

[24]  Hui Wang,et al.  Learning Naive Bayes Classifiers with Incomplete Data , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[25]  Luis Enrique Sucar,et al.  Learning Dynamic Naive Bayesian Classifiers , 2008, FLAIRS Conference.

[26]  Prakash P. Shenoy,et al.  A Comparison of Bayesian and Belief Function Reasoning , 2003, Inf. Syst. Frontiers.

[27]  Gary S. May,et al.  Real-time diagnosis of semiconductor manufacturing equipment using neural networks , 1995, Seventeenth IEEE/CPMT International Electronics Manufacturing Technology Symposium. 'Manufacturing Technologies - Present and Future'.

[28]  Benoît Iung,et al.  Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas , 2012, Eng. Appl. Artif. Intell..

[29]  Judea Pearl,et al.  Bayesian Networks , 1998, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[30]  Enrico Zio,et al.  Reliability engineering: Old problems and new challenges , 2009, Reliab. Eng. Syst. Saf..

[31]  Carlos A. Brizuela,et al.  Evolutionary Learning of Dynamic Naive Bayesian Classifiers , 2009, Journal of Automated Reasoning.

[32]  Kevin B. Korb,et al.  Bayesian Artificial Intelligence , 2004, Computer science and data analysis series.

[33]  Aaron D'Souza.  Using EM To Estimate A Probablity Density With A Mixture Of Gaussians , 2014 .

[34]  Stéphane Lafortune,et al.  Distributed Diagnosis of Discrete-Event Systems Using Petri Nets , 2003, ICATPN.

[35]  A. Ferreira,et al.  Virtual Metrology models for predicting physical measurement in semiconductor manufacturing , 2009, 2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference.

[36]  V. C. Jaswa,et al.  Architecture for control , 1984 .

[37]  Alessandro Giua,et al.  Diagnosis using labeled Petri nets: Faults may either be silent or undistinguishable events , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[38]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[39]  Philippe Weber,et al.  Reliability modelling with dynamic bayesian networks , 2003 .

[40]  E. Ruiz-Beltran,et al.  Fault diagnosis based on Petri net reduced models , 2006, 2006 3rd International Conference on Electrical and Electronics Engineering.

[41]  H.H. Aviles-Arriaga,et al.  Visual recognition of gestures using dynamic naive Bayesian classifiers , 2003, The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003..

[42]  M. Combacau,et al.  A hierarchical and modular structure for FMS control and monitoring , 1990, Proceedings [1990]. AI, Simulation and Planning in High Autonomy Systems.

[43]  Tom M. Mitchell,et al.  Bayesian Network Learning with Parameter Constraints , 2006, J. Mach. Learn. Res..

[44]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[45]  Didier Maquin,et al.  Observability, redundancy, reliability and integrated design of measurement systems , 1994 .

[46]  B Dhillon.  Methods for Performing Human Reliability and Error Analysis in Engineering Maintenance , 2009 .

[47]  Sang Jeen Hong,et al.  Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing $e$-Diagnostics , 2012, IEEE Transactions on Semiconductor Manufacturing.

[48]  Stéphane Lafortune,et al.  A DISTRIBUTED ALGORITHM FOR ON-LINE DIAGNOSIS OF PLACE-BORDERED PETRI NETS , 2005 .

[49]  Zoubin Ghahramani,et al.  Learning Dynamic Bayesian Networks , 1997, Summer School on Neural Networks.

[50]  Reynold Cheng,et al.  Naive Bayes Classification of Uncertain Data , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[51]  Marco Gori,et al.  Adaptive Processing of Sequences and Data Structures , 1998, Lecture Notes in Computer Science.

[52]  Fan-Tien Cheng,et al.  A Novel Virtual Metrology Scheme for Predicting CVD Thickness in Semiconductor Manufacturing , 2007, IEEE/ASME Transactions on Mechatronics.