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Proceedings Paper

Fuzzy selective voting classifier with defect extraction based on comparison within an image
Author(s): Toshifumi Honda; Ryo Nakagaki; Obara Kenji; Yuji Takagi
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Paper Abstract

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 different. 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%.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881Z (15 November 2007); doi: 10.1117/12.750528
Show Author Affiliations
Toshifumi Honda, Hitachi Ltd. (Japan)
Ryo Nakagaki, Hitachi Ltd. (Japan)
Obara Kenji, Hitachi High-Technologies Corp. (Japan)
Yuji Takagi, Hitachi Ltd. (Japan)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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