Abstract—In this paper, we propose a real-time fault detection system for the semiconductor domain, which aims to detect abnormal wafers from a recent history of electrical measurements. It is based on a dynamic model which uses our filter method as feature selection approach, and one-class support vector machines algorithm for classification task. The dynamicity of the model is ensured by updating the database through a temporal moving window. Two scenarios for updating the moving window are proposed. In order to prove the efficiency of our system, we compare it to an alternative detection system based on the Hotelling’s T2 test. Experiments are conducted on two real-world semiconductor datasets. Results show that our system outperforms the alternative system, and can provide an efficient way for real-time fault detection.
Index Terms—Real-time detection, feature selection, one-class support vector machines, semiconductor.
Hajj Hassan and S. Lambert-Lacroix are with the TIMC laboratory, Grenoble University, Grenoble, 38041 France (e-mail: {Ali.Hajj-Hassan, Sophie.Lambert}@imag.fr).
F. Pasqualini is with Process Control team, STMicroelectronics, Crolles, 38926 France (e-mail: Francois.Pasqualini@st.com).
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Cite:Ali Hajj Hassan, Sophie Lambert-Lacroix, and Francois Pasqualini, "Real-Time Fault Detection in Semiconductor Using One-Class Support Vector Machines," International Journal of Computer Theory and Engineering vol. 7, no. 3, pp. 191-196, 2015.
