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Virtual Equipment for benchmarking Predictive Maintenance algorithms

: Mattes, A.; Schöpka, U.; Schellenberger, M.; Scheibelhofer, P.; Leditzky, G.


Laroque, C. ; Institute of Electrical and Electronics Engineers -IEEE-; American Statistical Association -ASA-; Association for Computing Machinery -ACM-, Special Interest Group on Simulation; IEEE Systems, Man and Cybernetics Society -SMC-:
Winter Simulation Conference, WSC 2012. Proceedings. Vol.3 : Berlin, Germany, 9 - 12 December 2012; including the 8th International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM)
New York, NY: IEEE, 2012
ISBN: 978-1-4673-4779-2 (Print)
ISBN: 978-1-4673-4781-5 (Online)
Winter Simulation Conference (WSC) <2012, Berlin>
International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM) <8, 2012, Berlin>
Fraunhofer IISB ()

This paper presents a comparison of three algorithm types (Bayesian Networks, Random Forest and Linear Regression) for Predictive Maintenance on an implanter system in semiconductor manufacturing. The comparison studies are executed using a Virtual Equipment which serves as a testing environment for prediction algorithms prior to their implementation in a semiconductor manufacturing plant (fab). The Virtual Equipment uses input data that is based on historical fab data collected during multiple filament failure cycles. In an automated study, the input data is altered systematically, e.g. by adding noise, drift or maintenance effects, and used for predictions utilizing the created Predictive Maintenance models. The resulting predictions are compared to the actual time-to-failure and to each other. Multiple analysis methods are applied, resulting in a performance table.