Research Of Wafer Bonder Detection And Location System Based On Machine Vision

As the semiconductor manufacturing becoming more and more high-speed and low-cost, machine vision technology plays an increasingly important role in the integrated circuit packaging equipment of electronics industry. Among them, Wafer Bonder is an automated production equipment, which manufacturer of electronic components use to bring the crystalline silicon material from the wafer into corresponding position. Most important part of Wafer Bonder is Wafer Bonder detection and location system.Wafer Bonder detection and location system is a standard machine vision system, which is used to achieve correction and positioning of all crystal silicon wafer. By driving Wafer platform, identified crystalline silicon will be move to the position of mechanical arm and finally mechanical arm crawl and put them into position. Its core algorithm is tilt correction and positioning crystalline silicon material on the disc, the difficulty is the need for rapid and accurate completion of this process.In this paper, through analyzing a large number of wafer images, studying various systems related image processing algorithms, and finally realize a fast detection and location algorithm which can meet the system requirements. First, use wavelet pyramid to reduce the processing computation. Use the wavelet decomposition to process the input image, establish the wavelet pyramid, compare to direct the original image,the pyramid in the low-resolution image, there will be a significant reduction in the amount of computation, and low-resolution map is retained most of the original image information, so will not have too much interference to the next step of the process. Then use Hough transform line detection function, and structural features of silicon, we can bring about the wafer image tilt angle to achieve the tilt correction. Using Canny operator edge detection and mathematical morphology dilation, we can extract the edge features of the wafer image (including its apparent texture features) for further matching operations. The image can be horizontal and vertical projection by analyzing the arrangement of the law and the structural characteristics of crystalline silicon, respectively, the peak area in projection results is likely to be the area around the edge, which may filter out the location of crystalline silicon, exclude other locations, and reduce the amount of matching computation in the next step. Finally, we use the edge feature based template matching method, that is analyzing the edge of the silicon crystal to get some unchanged features in details, during the template matching, match crystalline silicon with multiple threshold set by these unchanged features, which rule out failure ones, then improve matching speed.