Spline regression based feature extraction for semiconductor process fault detection using support vector machine

Authors:
Jonghyuck Park;Ick-Hyun Kwon;Sung-Shick Kim;Jun-Geol Baek
Affiliations:
Graduate School of Information Management and Security, Korea University, Anam-dong, Seongbuk-gu, 136-701 Seoul, Republic of Korea;Department of Systems Management Engineering, Inje University, Gimhae, Gyeongnam 621-749, Republic of Korea;Division of Information Management Engineering, Korea University, Anam-dong, Seongbuk-gu, 136-701 Seoul, Republic of Korea;Division of Information Management Engineering, Korea University, Anam-dong, Seongbuk-gu, 136-701 Seoul, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Quality control is attracting more attention in semiconductor market due to harsh competition. This paper considers Fault Detection (FD), a well-known philosophy in quality control. Conventional methods, such as non-stationary SPC chart, PCA, PLS, and Hotelling's T^2, are widely used to detect faults. However, even for identical processes, the process time differs. Missing data may hinder fault detection. Artificial intelligence (AI) techniques are used to deal with these problems. In this paper, a new fault detection method using spline regression and Support Vector Machine (SVM) is proposed. For a given process signal, spline regression is applied regarding step changing points as knot points. The coefficients multiplied to the basis of the spline function are considered as the features for the signal. SVM uses those extracted features as input variables to construct the classifier for fault detection. Numerical experiments are conducted in the case of artificial data that replicates semiconductor manufacturing signals to evaluate the performance of the proposed method.