MACHINE LEARNING BASED DEFECT EXAMINATION AND RANKING FOR SEMICONDUCTOR SPECIMENS

    公开(公告)号:US20250124307A1

    公开(公告)日:2025-04-17

    申请号:US18488888

    申请日:2023-10-17

    Abstract: There is provided a system and method of defect examination on a semiconductor specimen. The method comprises: obtaining an inspection dataset informative of a group of defect candidates and attributes thereof resulting from examining the specimen by an inspection tool; classifying, by a classifier, the group of defect candidates into a plurality of defect classes such that each defect candidate is associated with a respective defect class; and ranking, by a decision model, the group of defect candidates into a total order using a sorting rule. Each defect candidate is associated with a distinct ranking in the total order representative of the likelihood of the defect candidate being a defect of interest (DOI). The decision model is previously trained to learn the sorting rule pertaining to the plurality of defect classes associated with the group of defect candidates and a series of attributes in the inspection data.

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