Evolving an Automatic Defect Classification Tool
- Cite this paper as:
- Glazer A., Sipper M. (2008) Evolving an Automatic Defect Classification Tool. In: Giacobini M. et al. (eds) Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg
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.