Fault Detection of Reactive Ion Etching Using Time Series Neural Networks
Maximizing the productivity in semiconductor manufacturing, early detection of process and/or equipment abnormality. Since most of the key processes in semiconductor production are performed under extremely high vacuum condition, no other action can be taken unless the undergoing process is terminated. In this paper, time series based neural networks have been employed to assist the decision for determining potential process fault in real-time. Principal component analysis (PCA) for the dimensionality reduction of the data is first performed to handle smoothly in real-time environment. According to the PCA, 11 system parameters were selected, and each of them were then classified using modeled and tested in time series. Successful detection on different types of process shift (or fault) was achieved with 0% false alarm.
KeywordsFalse Alarm Fault Detection Control Limit Tool Data Advanced Process Control
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- 1.Hong, S., May, G., Park, D.: Neural Network Modeling of Reactive Ion Etch Using Optical Emission Spectroscopy Data. IEEE Trans. Semi. Manufac. 16(4), 1–11 (2003)Google Scholar
- 2.Montgomery, D.: Introduction to Statistical Quality Control. Wiley, New York (1991)Google Scholar
- 3.Guo, H., Spanos, C., Miller, A.: Real Time Statistical Process Control for Plasma Etching. In: Semiconductor Manufacturing Science Symposium, pp. 113–118 (1991)Google Scholar
- 4.Barna, G.: APC in the Semiconductor Industry, History and Near Term Prognosis. In: Proc. IEEE/SEMI Adv. Semi. Manufac. Conf., pp. 364–369 (1996)Google Scholar
- 5.Goodlin, B., Boning, D., Sawin, H.: Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools. In: Proc. Int. Symposium on Plasma Processing XIV (2002)Google Scholar
- 6.Yue, H., Qin, s., Markle, R., Nauert, C., Gatto, M.: Fault Detection of Plasma Etchers Using Optical Emission Spectra. IEEE Trans. Semi. Manufac. 13(3) (2000)Google Scholar