Evolving an Automatic Defect Classification Tool

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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.
Assaf Glazer, Moshe Sipper
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where EVOW
Authors Assaf Glazer, Moshe Sipper
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