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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Defects Extraction for QFN Based on Texture Detection and Region of Interest Selection
verfasst von : Kai Chen, Zhisheng Zhang, Yuan Chao, Fuyun He, Jinfei Shi
Erschienen in: Advanced Multimedia and Ubiquitous Engineering
Verlag: Springer Singapore
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Abstract
On the surface of the quad flat non-lead (QFN) dark-filed images, noise pixels (including textures produced in the molding process) obstruct the defect inspection. To extract defects from QFN surface, a novel method based on texture detection and region of interest selection is proposed. Firstly, a QFN texture direction detector is proposed. Secondly, multilevel thresholding method is used to segment QFN images. Thirdly, according to the image level, the bright defects images and the dark defect images are obtained. Then, the region of interest selection method is applied to reserving defects regions and removing QFN textures and noise pixels. Finally, our method extracts defects by combining the bright and dark defects image. The experiments show that the proposed method can extract defects efficiently.