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

    公开(公告)号：US11449977B2

    公开(公告)日：2022-09-20

    申请号：US16942677

    申请日：2020-07-29

    Applicant: Applied Materials Israel Ltd.

    Inventor： Matan Steiman ,  Shalom Elkayam

    IPC: G06N20/00 ,  G06K9/62 ,  G06T7/00 ,  G06T7/10 ,  G06N3/04 ,  G06F17/18

    Abstract: There is provided a system and method of generating training data for training a Deep Neural Network usable for examination of a semiconductor specimen. The method includes: obtaining a first training image and first labels respectively associated with a group of pixels selected in each segment, extract a set of features characterizing the first training image, train a machine learning (ML) model using the first labels, values of the group of pixels, and the feature values of each of the set of features corresponding to the group of pixels, process the first training image using the trained ML model to obtain a first segmentation map, and determine to include the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.

    Generating a training set usable for examination of a semiconductor specimen
    3.
    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
    6.
    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
    7.
    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.

    GENERATING TRAINING DATA USABLE FOR EXAMINATION OF A SEMICONDUCTOR SPECIMEN
    8.
    发明申请
    GENERATING TRAINING DATA USABLE FOR EXAMINATION OF A SEMICONDUCTOR SPECIMEN 有权

    公开(公告)号：US20220383488A1

    公开(公告)日：2022-12-01

    申请号：US17886191

    申请日：2022-08-11

    Applicant: Applied Materials Israel Ltd.

    Inventor： Matan Steiman ,  Shalom Elkayam

    IPC: G06T7/00 ,  G06N20/00 ,  G06T7/10 ,  G06N3/04 ,  G06K9/62 ,  G06F17/18

    Abstract: Provided is a system and method of generating training data for training a Deep Neural Network usable for examination of a semiconductor specimen. The method includes: obtaining a first training image and first labels respectively associated with a group of pixels selected in each segment, extract a set of features characterizing the first training image, train a machine learning (ML) model using the first labels, values of the group of pixels, and the feature values of each of the set of features corresponding to the group of pixels, process the first training image using the trained ML model to obtain a first segmentation map, and determine to include the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.

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