Evolving an Automatic Defect Classification Tool

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


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.


Genetic Algorithm Radial Basis Function Neural Network Radial Basis Function Network Hide Unit Basic Evolutionary Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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