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The EUV (Extreme Ultraviolet) lithography is certainly technology for 10nm or less which was used to mass-produce chips contributes to improving the minimum feature size, reducing the process step by enabling DPT (Double Patterning Technology)-less, and improving Fab. operation. Due to the expansion of EUV layers in mass production of below 5nm, EUV mask layers are continuously increasing for mass product. In order to EUV mask mass product, it is important to investment of very high expensive EUV tools and facilities, improve longterm TAT (Turn Around Time), and management of challenge yields. Among the manager of stable yields which are performed just after every process to minimize the continuous defect of mask during the process handling, take an inspection every single mask is the best way. However, investment in inspection tools and increase in inspection step are not efficient due to inefficient factory operation due to very high cost and long TAT delay. Currently most mask manufacturing companies are using manual visual inspection by human eyes and microscope. In this paper, quality monitoring system was developed to detect micro-unit front and back defects, scratches, contaminations, and coating defects by applying the Image Segmentation technique to photos taken on the front and back of the mask by modern load port (refer to wafer EFEM: End Front Equipment Module). In an effort to understand further, the authors evaluated three image segmentations technologies using CNN (Convolutional Neural Network), Sobel Edge Detector, AI (Artificial Intelligence) for mask yield managing program. This method can provide the means for determining scraps and analyzing complex log files for a quality issue found in mask fabrication. These challenges will make a paradigm shift in mask industry for the EUV mask mass product to make chips. The mask tool manufacturers unify load port specifications, it will be able to contribute to new technology and process improvement in the future.
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