Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces

• Published in: IEEE transactions on industrial informatics Vol. 7; no. 1; pp. 125 - 135
• Main Authors: Tsai, Du-Ming, Luo, Jie-Yu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; Conversion; Crystal defects; Defect detection; Entropy; Grain structure; Image edge detection; Inspection; Kernel; machine vision; mean shift; multicrystalline solar wafer; Pixel; surface inspection; Surface layer; Surface texture; Tasks; Texture; Wafers
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2010.2092783

This paper presents an automated visual inspection scheme for multicrystalline solar wafers using the mean-shift technique. The surface quality of a solar wafer critically determines the conversion efficiency of the solar cell. A multicrystalline solar wafer contains random grain structures and results in a heterogeneous texture in the sensed image, which makes the defect detection task extremely difficult. Mean-shift technique that moves each data point to the mode of the data based on a kernel density estimator is applied for detecting subtle defects in a complicated background. Since the grain edges enclosed in a small spatial window in the solar wafer show more consistent edge directions and a defect region presents a high variation of edge directions, the entropy of gradient directions in a small neighborhood window is initially calculated to convert the gray-level image into an entropy image. The mean-shift smoothing procedure is then performed on the entropy image to remove noise and defect-free grain edges. The preserved edge points in the filtered image can then be easily identified as defective ones by a simple adaptive threshold. Experimental results have shown the proposed method performs effectively for detecting fingerprint and contamination defects in solar wafer surfaces.