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Semiconductor Process Inspection Using Mask R-CNN  

Han, Jung Hee (Graduate School of Convergence Science and Technology(GSCST), Seoul National University)
Hong, Sung Soo (Department of Electrical and Computer Engineering, Seoul National University)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.3, 2020 , pp. 12-18 More about this Journal
Abstract
In semiconductor manufacturing, defect detection is critical to maintain high yield. Currently, computer vision systems used in semiconductor photo lithography still have adopt to digital image processing algorithm, which often occur inspection faults due to sensitivity to external environment. Thus, we intend to handle this problem by means of using Mask R-CNN instead of digital image processing algorithm. Additionally, Mask R-CNN can be trained with image dataset pre-processed by means of the specific designed digital image filter to extract the enhanced feature map of Convolutional Neural Network (CNN). Our approach converged advantage of digital image processing and instance segmentation with deep learning yields more efficient semiconductor photo lithography inspection system than conventional system.
Keywords
Semiconductor Photo Lithography Inspection; Object Segmentation; Instance Segmentation; Digital Image Processing; Computer Vision System; Deep Learning; Convolutional Neural Network;
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Times Cited By KSCI : 4  (Citation Analysis)
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