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    Generating a training set usable for examination of a semiconductor specimen
    2.
    发明授权
    Generating a training set usable for examination of a semiconductor specimen 有权

    公开(公告)号：US11232550B2

    公开(公告)日：2022-01-25

    申请号：US16916047

    申请日：2020-06-29

    Applicant: Applied Materials Israel Ltd.

    Inventor： Elad Ben Baruch ,  Shalom Elkayam ,  Shaul Cohen ,  Tal Ben-Shlomo

    IPC: G06T7/00 ,  G06K9/62 ,  G06T7/143

    Abstract: There is provided a system and method of generating a training set for training a Deep Neural Network usable for examination of a specimen. The method includes: for each given training image in a group: i) generating a first batch of training patches, including cropping the given training image into a first plurality of original patches; and augmenting at least part of the first plurality of original patches in order to simulate variations caused by a physical process of the specimen; and ii) generating a second batch of training patches, including: shifting the plurality of first positions on the given training image to obtain a second plurality of original patches, and repeating the augmenting to the second plurality of original patches to generate a second plurality of augmented patches; and including at least the first second batches of training patches corresponding to each given training image in the training set.

    Machine learning based examination of a semiconductor specimen and training thereof
    3.
    发明授权
    Machine learning based examination of a semiconductor specimen and training thereof 有权

    公开(公告)号：US12260543B2

    公开(公告)日：2025-03-25

    申请号：US17706306

    申请日：2022-03-28

    Applicant: Applied Materials Israel Ltd.

    Inventor： Tal Ben-Shlomo ,  Shalom Elkayam ,  Shaul Cohen ,  Tomer Peled

    IPC: G06T7/00 ,  G06T7/11

    Abstract: There is provided a system and method of runtime examination of a semiconductor specimen. The method includes obtaining a runtime image representative of an inspection area of the specimen, the runtime image having a relatively low signal-to-noise ratio (SNR); and processing the runtime image using a machine learning (ML) model to obtain examination data specific for a given examination application, wherein the ML model is previously trained for the given examination application using one or more training samples, each training sample representative of a respective reference area sharing the same design pattern as the inspection area and comprising: a first training image of the respective reference area having a relatively low SNR; and label data indicative of ground truth in the respective reference area pertaining to the given examination application, the label data obtained by annotating a second training image of the respective reference area having a relatively high SNR.

    Segmentation of an image of a semiconductor specimen
    4.
    发明授权
    Segmentation of an image of a semiconductor specimen 有权

    公开(公告)号：US11631179B2

    公开(公告)日：2023-04-18

    申请号：US16917692

    申请日：2020-06-30

    Applicant: Applied Materials Israel Ltd.

    Inventor： Elad Ben Baruch ,  Shalom Elkayam ,  Shaul Cohen ,  Tal Ben-Shlomo

    IPC: G06N20/00 ,  G06T7/11 ,  G06T7/187 ,  G06F17/18 ,  G06F30/27

    Abstract: There is provided a system and method of segmenting an image of a fabricated semiconductor specimen. The method includes: obtaining a first probability map corresponding to the image representative of at least a portion of the fabricated semiconductor specimen and indicative of predicted probabilities of pixels in the image to correspond to one or more first structural elements presented in the image, obtaining a first label map informative of one or more segments representative of second structural elements and labels associated with the segments, performing simulation on the first label map to obtain a second probability map indicative of simulated probabilities of pixels in the first label map to correspond to the one or more segments, and generating a second label map based on the first probability map and the second probability map, the second label map being usable for segmentation of the image with enhanced repeatability.

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