Abstract
Wafer dicing is a basic step in building electronic devices such as CPU or GPU. It is automatically accomplished in the wafer Dicing Machine in which a little misalignment of wafers may lead to damaged chips. The information of misalignment may be obtained by detecting the paddle vat to calculate the misalignment between the blade and the part of the wafer which has been diced in real-time. In this paper, methods for detecting the paddle vat are studied. For most of simple wafer images where the intensities of paddle vats are quite different from that of the background, a binary segmentation approach is used to detect the paddle vat of the wafer image. The threshold for binarization is calculated by the grey-scale histogram of the wafer image which is smoothed by using exponentially smoothed method. For wafer images with complex backgrounds, the improved Canny detection algorithm with anisotropic diffusion is used to detect the edges of paddle vats. Experimental results show the validity of the methods.
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