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

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. Keywords —Autoregressive Integrated Moving Average, Dynamic Time Warping, Fault Detection, Seasonal Autoregressive Integrated Moving Average, Semiconductor Process, Time Series Modeling 1. I NTRODUCTION Over the past decade of continuous improvements being made to semiconductor technologies, the device feature size has been scaled down tremendously. As a result, the processes have become more and more complex, and this calls for a much tighter process control than before. The faults in the manufacturing processes must be detected with precision to reduce the loss in manufacturing. Semiconductor equipment data has been shown to be helpful for the detection of any abnormal variation in the process. This is because any fault occurring during the process appears as some variation in the equipment tool data collected for that process. There has been active research conducted in the fault detection area by applying different statistical techniques to the tool data. Neural networks have been one of the most researched techniques for the purpose [1-3]. Principal component analysis (PCA) and its variants have been widely employed as well [4,5]. Control charts have also been used for simultaneous fault detection and classification [6]. The use of autoregressive moving average based time series modeling techniques have also been shown to be quite useful in this regard [7,8]. These works have shown that the analysis of tool data gives greater insight into the process variations and can be used to detect abnormalities in the process. In this research, the seasonal autoregressive integrated moving average (SARIMA) time series models are investigated for the detection of faults in etch tool data. The derivative dynamic time