Following mask inspection, mask-defect classification is a process of reviewing and classifying each captured defect according to prior-defined printability rules. With the current hardware configuration in manufacturing environments, this review and classification process is a mandatory manual task. For cases with a relatively small number of captured defects, defect classification itself does not put too much burden to operators or engineers. With a moderate increase of defects, it would, however, become a time-consuming process and prolong the total mask-making cycle time. Should too many nuisance defects be caught under a given detection sensitivity, engineers would generally loosen the detection sensitivity in order to reduce the number of nuisance defects. By doing that, however, there exists potential threat of missing real defects. The present study describes a "progressive self-learning" (PSL) algorithm for defect classification to relieve loading from operators or engineers and further accelerate defect review/classification process. Basically, the PSL algorithm involves with image extraction, digitization, alignment and matching. One key concept of this PSL algorithm is that there is not any pre-stored defect library in the first place of a particular run. In turn, a defect library is "progressively" built during the initial stage of defect review and classification at each run. The merit of this design can be realized by its flexibility. An additional benefit is that all defect images are stored and suitable for network transfer. The C language is adopted to implement the present algorithm to avoid the porting issue, so as not bound to a particular machine. Assessment of the PSL algorithm is examined in terms of efficiency and the accurate rate.
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