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A Hybrid ANN-FIR System for Lot Output Time Prediction and Achievability Evaluation in a Wafer Fab

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Analysis and Design of Intelligent Systems using Soft Computing Techniques

Part of the book series: Advances in Soft Computing ((AINSC,volume 41))

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Abstract

A hybrid artificial neural network (ANN)-fuzzy inference rules (FIR) system is constructed in this study for lot output time prediction and achievability evaluation in a fabrication plant (wafer fab), which are critical tasks to the wafer fab. At first, a hybrid and recurrent ANN, i.e. self-organization map (SOM) and fuzzy back propagation network (FBPN), is proposed to predict the output time of a wafer lot. According to experimental results, the prediction accuracy of the hybrid ANN was significantly better than those of some existing approaches. Subsequently, a set of fuzzy inference rules is established to evaluate the achievability of an output time forecast.

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Patricia Melin Oscar Castillo Eduardo Gomez Ramírez Janusz Kacprzyk Witold Pedrycz

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© 2007 Springer-Verlag Berlin Heidelberg

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Chen, T. (2007). A Hybrid ANN-FIR System for Lot Output Time Prediction and Achievability Evaluation in a Wafer Fab. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_24

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  • DOI: https://doi.org/10.1007/978-3-540-72432-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72431-5

  • Online ISBN: 978-3-540-72432-2

  • eBook Packages: EngineeringEngineering (R0)

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