Fault Detection in the Semiconductor Etch Process Using the Seasonal Autoregressive Integrated Moving Average Modeling
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In this paper, we investigated the use of seasonal autoregressive integratedmoving average (SARIMA) time series models for fault detection in semiconductor etchequipment data. The derivative dynamic time warping algorithm was employed for thesynchronization of data. The models were generated using a set of data from healthyruns, and the established models were compared with the experimental runs to find thefaulty runs. It has been shown that the SARIMA modeling for this data can detect faultsin 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 semiconductorfabrication.
Abstract
In this paper, we investigated the use of seasonal autoregressive integratedmoving average (SARIMA) time series models for fault detection in semiconductor etchequipment data. The derivative dynamic time warping algorithm was employed for thesynchronization of data. The models were generated using a set of data from healthyruns, and the established models were compared with the experimental runs to find thefaulty runs. It has been shown that the SARIMA modeling for this data can detect faultsin 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 semiconductorfabrication.
참고문헌 (15)
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