Evolving an Automatic Defect Classification Tool

  • Assaf Glazer
  • Moshe Sipper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)

Abstract

Automatic Defect Classification (ADC) is a well-developed technology for inspection and measurement of defects on patterned wafers in the semiconductors industry. The poor training data and its high dimensionality in the feature space render the defect-classification task hard to solve. In addition, the continuously changing environment—comprising both new and obsolescent defect types encountered during an imaging machine’s lifetime—require constant human intervention, limiting the technology’s effectiveness. In this paper we design an evolutionary classification tool, based on genetic algorithms (GAs), to replace the manual bottleneck and the limited human optimization capabilities. We show that our GA-based models attain significantly better classification performance, coupled with lower complexity, with respect to the human-based model and a heavy random search model.

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References

  1. 1.
    David Sanchez, A.V.: Searching for a solution to the automatic RBF network design problem. Neurocomputing 42, 147–170 (2002)MATHCrossRefGoogle Scholar
  2. 2.
    Bilmes, J.: A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models (1997)Google Scholar
  3. 3.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  4. 4.
    Buchtala, O., Klimek, M., Sick, B.: Evolutionary optimization of radial basis function classifiers for data mining applications. IEEE Transactions on Systems, Man and Cybernetics, Part B 35, 928–947 (2005)CrossRefGoogle Scholar
  5. 5.
    Camacho, F., Manrique, D., Rodriguez-Paton, A.: Designing radial basis function networks with genetic algorithms. In: IASTED International conference artificial intelligence and soft computing, September 2004, vol. 451(8), pp. 398–403 (2004)Google Scholar
  6. 6.
    Kuncheva, L.I.: Initializing of an RBF network by a genetic algorithm. Neurocomputing 14, 273–288 (1997)CrossRefGoogle Scholar
  7. 7.
    Kuo, L.E., Melsheimer, S.S.: Using genetic algorithms to estimate the optimum width parameter in radial basis function networks. In: American Control Conference, vol. 2, pp. 1368–1372 (July 1994)Google Scholar
  8. 8.
    Maillard, E.P., Gueriot, D.: RBF neural network, basis functions and genetic algorithm. In: Neural Networks International Conference, vol. 4, pp. 2187–2192 (June 1997)Google Scholar
  9. 9.
    Wai Mak, M., Wai Cho, K.: Genetic evolution of radial basis function centers for pattern classification, citeseer.ist.psu.edu/8,1322.html
  10. 10.
    Specht, D.F.: Probabilistic neural networks. Neural networks 3, 109–118 (1990)CrossRefGoogle Scholar
  11. 11.
    Whitehead, B.A., Choate, T.D.: Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Transactions on Neural Networks 7, 869–880 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Assaf Glazer
    • 1
    • 2
  • Moshe Sipper
    • 1
  1. 1.Dept. of Computer ScienceBen-Gurion UniversityBeer-ShevaIsrael
  2. 2.Applied Materials, Inc.RehovotIsrael

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