Automatic LED chips defect inspection using level set method of image segmentation

Zhongliang Pan; Ling Chen

doi:10.1117/12.2201142

3 December 2015 Automatic LED chips defect inspection using level set method of image segmentation

Zhongliang Pan, Ling Chen

Proceedings Volume 9794, Sixth International Conference on Electronics and Information Engineering; 97941W (2015) https://doi.org/10.1117/12.2201142
Event: Sixth International Conference on Electronics and Information Engineering, 2015, Dalian, China

Abstract

Several types of defects in the LED chips may be caused from the manufacturing environments, these defects can result in the degradations of the parameters and performance. Hence it is very necessary to perform the defect detection of LED chips for effectively enhancing the production quality. In this paper, a new method is presented for the defect inspection of LED chips. The method makes use of both the support vector machine and the image segmentation based on level set to acquire the features of LED chip images and to carry out the inspection. First of all, the support vector machine is used to perform the clustering for the original LED images, the margin of region clustering is used as the initial contour curve. Secondly, the segmentation using level set is implemented for the LED images. The features of LED images are extracted, then the database of features is constructed. Therefore, an automatic inspection system for LED chip is built, the system can recognize the defective LED chips. The experimental results demonstrate that the presented method in this paper is able to detect the defects in the LED chips accurately.

Citation Download Citation

Zhongliang Pan and Ling Chen "Automatic LED chips defect inspection using level set method of image segmentation", Proc. SPIE 9794, Sixth International Conference on Electronics and Information Engineering, 97941W (3 December 2015); https://doi.org/10.1117/12.2201142

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KEYWORDS

Light emitting diodes

Image segmentation

Defect inspection

Defect detection

Inspection

Semiconducting wafers

Neural networks

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