Tape substrate (TS) product is a high-density circuit pattern on thin film substrate, and it requires precise and high resolution imaging system for inspection. We introduce here a TS inspection system developed, where the products are fed through a reel to reel system, and a series of inspection algorithms based on a referential method. In the system, it is so hard to achieve consistent images for such a thin and flexible materials as TS product that the images suffer from individual, local distortion during the image acquisition. Since the distortion results in relatively big discrepancy between an inspection image and the master one, direct image to image comparison approach is not available for inspection. To inspect the pattern in a more robust way in this application, we propose a graph matching method where the patterns are modeled as a collection of lines with link points as features. In the offline teaching process, the graph model is achieved from skeleton of the master image, which is collected as a data base. In the run time, a boundary tracking method is used for extracting the graph model from an inspection image instead of a skeleton process to reduce the computation time. By comparing the corresponding graph models, a line that is linked to undesired endpoints can be detected, which becomes an open or short defect. Through boundary tracking approach, we can also detect boundary defects such as pattern nick and protrusions as well.
Convolution of an image is indispensable in many image processing applications, but it is a time-consuming process. In general the convolution mask is restricted the size of 15 by 15 because of its computation time. Many approaches are attempted to reduce the convolution processing time using hardware and software algorithms. But they are restricted in specific application. In this paper, a novel approach is presented. This method is realized by simplifying the convolution process. The convolution mask is approximated and decomposed to more simple form, K convolutions with constant mask value respectively. K is the number of levels of approximation, which is less than or equal to (N+1)/2, where N is the original mask size. Calculation process is reconstructed to reduce recursive multiplications. And cumulative image, which contains sum of pixel values of the rectangle area from the origin to each pixel, is prepared for this process. Processing time is dramatically reduced and resulting image is similar to the one by original convolution mask. For 13 by 13 mask convolution, new method is above 20 times faster than conventional one.
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