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In recent years automated patterned wafer inspection systems have been replacing manual inspection in semiconductor manufacturing facilities. Smaller features and larger devices have made manual inspection inefficient if not impossible, and several commercial systems have been developed to meet the needs of the semiconductor industry. The defect detection techniques used by these systems can be divided into reference comparison and spatial filtering. Reference comparison is the most common. The reference can be another chip on the same wafer or a golden part [4] . Spatial filtering has been successfully applied to the inspection of repetitive patterns [7]. A third inspection technique based on design rule checks is common in printed circuit board inspection but has yet to be successfully applied to integrated circuits, which present a much more complex and variable pattern due to the small feature size and multiple levels. The reference and spatial filter based automatic systems far exceed the performance of human inspectors. Current systems inspect a single chip in minutes, detecting defects as small as one half micron, and in the near future machines will take only seconds to inspect a chip for quarter micron defects. In one respect only, does the human inspector's performance exceed that of the machine: To date, no automatic patterned wafer inspection system is capable of filtering out real defects from nuisance defects such as granularity in metal or polysilicon. Humans, however, are able to detect defects in grainy, textured fields with little difficulty. Furthermore, for humans no knowledge of the pattern is needed to detect obvious anomalies such as breaks or neckdowns in lines. Conversations with people who inspect patterned wafers indicate that they do not serially scan the pattern for defects. Instead they allow their gaze to relax as they move the pattern under the microscope objective. When a defect comes into the field of view, they report that it "pops out" , grabbing their attention. This phenomenon is characteristic of preattentive or effortless vision, a subject that has been extensively studied in psychophysics. Preattentive vision is generally considered to be based on the decomposition of the visual input into a limited set of features which are detected in parallel. Since these operations extend well beyond the foveal or high resolution area of the visual field, one may assume that they are based on lower resolution features. Such parallelism and data reduction imply computationally efficient processing that could be emulated for machine vision pattern recognition purposes. It was with this in mind that we launched a project aimed at developing new pattern inspection techniques based on models of human preattentive vision. This paper will first briefly describe current theories of preattentive vision. It will then outline the model used as a basis on which to develop the pattern inspection techniques. Lastly it will discuss two defect detection techniques, which will be illustrated with examples.
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