Semiconductor visual inspection is necessary for production yield control. Defect classification is a key procedure in
determing defect sources. Auttomization of this procedure is required in order to achieve efficient and high-yield
production. In the present paper, an automatic defect classification (ADC) algorithm for a semiconductor inspection is
proposed. The ADC algorithm consists of the following three parts;
1) A defect extraction algorithm to achieve high-sensitivity defect extraction even in regions in which the brightness is
unstable due to optical interference at a thin layer.
2) An appearance feature calculation from a color image inside the defect region extracted from 1).
3) A unique training type classifier called the fuzzy selective voting classifier (FSVC), which calculates the weight for
each appearance feature in order to achieve accurate classification even when the discriminancy of each feature is
The performance of the developed ADC algorithm has been evaluated using defect acquired from an actual production
line. The accuracy of the classification was 85.9% and the false rejection rate was 93%.