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

Jonghyuck Park, Ick Hyun Kwon, Sung Shick Kim, Jun Geol Baek

Research output: Contribution to journalArticle

19 Citations (Scopus)


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 T2, 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.

Original languageEnglish
Pages (from-to)5711-5718
Number of pages8
JournalExpert Systems With Applications
Issue number5
Publication statusPublished - 2011 May 1



  • Fault detection
  • Feature extraction
  • Semiconductor manufacturing
  • Spline regression
  • Support vector machine

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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