MACHINE LEARNING-BASED DEFECT DETECTION OF A SPECIMEN

    公开(公告)号:US20210209418A1

    公开(公告)日:2021-07-08

    申请号:US16733219

    申请日:2020-01-02

    IPC分类号: G06K9/62 G06T7/00

    摘要: There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

    MACHINE LEARNING BASED DEFECT EXAMINATION FOR SEMICONDUCTOR SPECIMENS

    公开(公告)号:US20240338811A1

    公开(公告)日:2024-10-10

    申请号:US18130845

    申请日:2023-04-04

    IPC分类号: G06T7/00 G06N3/0455

    摘要: There is provided a system and method of examination a semiconductor specimen. The method includes obtaining a runtime image of the specimen; processing the runtime image using a first machine learning (ML) model to extract a set of runtime features representative of a set of patches in the runtime image; and comparing the set of runtime features with a bank of reference features, giving rise to an anomaly map indicative of one or more defective patches in the runtime image. The bank of reference features is previously generated by obtaining a plurality of synthetic reference images generated by a second ML model based on a plurality of actual images; and processing the plurality of synthetic reference images by the first ML model to extract, for each synthetic reference image, a set of reference features representative thereof, giving rise to the bank of reference features.