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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Time Series Fault Prediction in Semiconductor Equipment Using Recurrent Neural Network
verfasst von : Javeria Muhammad Nawaz, Muhammad Zeeshan Arshad, Sang Jeen Hong
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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This paper presents a model of Elman recurrent neural network (ERNN) for time series fault prediction in semiconductor etch equipment. ERNN maintains a copy of previous state of the input in its context units, as well as the current state of the input. Derivative dynamic time warping (DDTW) method is also discussed for the synchronization of time series data set acquired from plasma etcher. For each parameter of the data, the best ERNN structure was selected and trained using Levenberg Marquardt to generate one-step-ahead prediction for 10 experimental runs. The faulty experimental runs were successfully distinguished from healthy experimental runs with one missed alarm out of ten experimental runs.