Evolving an Automatic Defect Classification Tool
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.
Unable to display preview. Download preview PDF.
- 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.Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
- 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
- 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.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.Wai Mak, M., Wai Cho, K.: Genetic evolution of radial basis function centers for pattern classification, citeseer.ist.psu.edu/8,1322.html