Most current commercial automated IC inspection systems use gray-level or binary images for IC defect detection in spite of the fact that color permits defect detection where gray-level information is insufficient. Three color image processing techniques including the spectral-spatial clustering, principal components, and hue-saturation-value (HSV) color features have been investigated to evaluate the usefulness of color for IC defect detection. The AMOEBA spectral-spatial clustering algorithm, an un-supervised color segmentation approach, with a sequence of image processing procedures resulted in segmentation results with high accuracy and discriminated successfully an isolated and homogeneous defect with an unique color signature. The principal components transformation and the HSV color features, two color enhancement/separation algorithms, have proven useful for enhancing and isolating weak spectral signatures in the defect regions. The results of this investigation into the use of color are promising.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/276831 |
| Date | January 1988 |
| Creators | Yang, Hsien-Min, 1957- |
| Contributors | Schowengerdt, Robert |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Thesis-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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