Fault Detection in the Semiconductor Etch Process Using the Seasonal Autoregressive Integrated Moving Average Modeling

Muhammad Zeeshan Arshad, Javeria Muhammad Nawaz and Sang Jeen Hong
Volume: 10, No: 3, Page: 429 ~ 442, Year: 2014
10.3745/JIPS.04.0004
Keywords: Autoregressive Integrated Moving Average, Dynamic Time Warping, Fault Detection, Seasonal Autoregressive Integrated Moving Average, Semiconductor Process, Time Series Modeling
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Abstract
In this paper, we investigated the use of seasonal autoregressive integrated moving average (SARIMA) time series models for fault detection in semiconductor etch equipment data. The derivative dynamic time warping algorithm was employed for the synchronization of data. The models were generated using a set of data from healthy runs, and the established models were compared with the experimental runs to find the faulty runs. It has been shown that the SARIMA modeling for this data can detect faults in the etch tool data from the semiconductor industry with an accuracy of 80% and 90% using the parameter-wise error computation and the step-wise error computation, respectively. We found that SARIMA is useful to detect incipient faults in semiconductor fabrication.

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Cite this article
IEEE Style
Muhammad Zeeshan Arshad, Javeria Muhammad Nawaz, and Sang Jeen Hong, "Fault Detection in the Semiconductor Etch Process Using the Seasonal Autoregressive Integrated Moving Average Modeling," Journal of Information Processing Systems, vol. 10, no. 3, pp. 429~442, 2014. DOI: 10.3745/JIPS.04.0004.

ACM Style
Muhammad Zeeshan Arshad, Javeria Muhammad Nawaz, and Sang Jeen Hong, "Fault Detection in the Semiconductor Etch Process Using the Seasonal Autoregressive Integrated Moving Average Modeling," Journal of Information Processing Systems, 10, 3, (2014), 429~442. DOI: 10.3745/JIPS.04.0004.