Evolving an automatic defect classification tool

Authors:
Assaf Glazer;Moshe Sipper
Affiliations:
Dept. of Computer Science, Ben-Gurion University, Beer-Sheva, Israel and Applied Materials, Inc., Rehovot, Israel;Dept. of Computer Science, Ben-Gurion University, Beer-Sheva, Israel
Venue:
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Year:
2008

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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.