Study On Image Detection And Automatic Technology Of Deep-Submicrometer IC

As semiconductor technics is smaller and smaller, having arrived to the deep-submicrometer level. Wafer's CD is decreasing while its area is increasing that leads to defect density is lower and lower as well as hard to distinguish true or false defect. From the theoretical angle, it is not difficult to catch a single-chip image when wafer image is gathered. Because deep-submicrometer wafer is the more highly precise product and chip density is higher, the image gathering system must work in the highly amplified multiples in order to catch the high-resolution image, but field of vision is relatively small while images gathered increase with square speed. Obviously, the large image data restrict the efficiency of wafer quality detection.The paper is on the basis of the science and technology item of Guangdong province in 2004: Automatic inspection system of IC wafer micrograph based on digital image process. It aims at the contradiction between the large image data and the real-time requirement of detection. And it synthetically uses technology of digital image processing, AOI technology, database technology, theory of pattern match and pattern recognition etc., mainly studying in some key problems such as strategy of image detection, automatically analytical algorithm and management of characteristic parameter etc.The paper deep analyzes the relative technology difficulties of image detection and automatic analysis for deep-submicrometer wafer from algorithm theory and practical application, its main contents as follows:Studying and experimenting the strategies for image detection. It uses the key region and the coarse to precise detection method to detect wafer not all over according to the experience and knowledge. And it recognizes different defects by relevant algorithm of image detection aiming to different detection objects. As for the regular images, it uses the detection method of pattern match while using the detection method of human and PC for the non-regular ones.Using the projection theorem and detection method based on pixel character to get the pixel distribution of projection transfer, and successfully detect the redundancymaterial defect by detecting the distributed character of pixel. And it uses the detection method based on Hough and skeleton to recognize the dropped material defect and ascertain the invalidate forms of chip after skeleton extraction.Coming up the automatic recognition method for defects.lt orients the object region by method of image search based on the region characteristic and uses SSDA to successfully search the object region by choosing the suitable matching template, on the basis of these, processing and analyzing the object image and recognizing it.Finally, taking detection of welding quality as an example to verify the feasibility of the automatic recognition method.Using the model of three-layer configuration to construct system's database.And it improves the compatibility of image processing and analysis, restore of detection result, data output etc. Finding out the interesting and useful information by technology of data extraction based on description, as well as outputting analysis reports of detection results.