@article{MD40A3E46,
title = "Multiple Defect pattern Recognition in a Wafer Map Using Vector-Representation Based Capsule Network",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2023",
issn = "1226-4717",
doi = "10.7840/kics.2023.48.9.1179",
author = "Misun Kim, Ji Hwan Choi, Harim Lee",
keywords = "Vector representation, CapsNet, Learning, Machine Learning, Deep Learning",
abstract = "To satisfy the demand of semiconductor market, the semiconductor manufacturing process should guarantee the production of high-yield and high-quality semiconductors. The fabrication process is complexly compound of several sub-processes, and thus even if an experienced engineer manages the process with precise equipment in a clean environment, it is difficult to make a wafer with no error-free dies. Therefore, the engineer should quickly discover which sub-process is mal-functioning for high yield. Fortunately, error dies make a specific defect pattern, which corresponds to specific abnormal operation of some fabrication sub-processes. Hence, a scheme that recognizes defect patterns in a wafer map can allow fabrication engineers to make the high-quality wafer with few error dies. In this paper, we implement a capsule network based multiple-defect recognition scheme with high precision and recall per each defect pattern. This work is the first to exploit a vector-representation based network for the recognition of defects in a wafer map, and verifies that the network using vector representation shows higher performance compared to the convontional feature-map based networks."
}