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    MACHINE LEARNING-BASED CLASSIFICATION OF DEFECTS IN A SEMICONDUCTOR SPECIMEN
    12.
    发明申请
    MACHINE LEARNING-BASED CLASSIFICATION OF DEFECTS IN A SEMICONDUCTOR SPECIMEN 有权

    公开(公告)号：US20220222806A1

    公开(公告)日：2022-07-14

    申请号：US17605217

    申请日：2020-03-24

    Applicant: APPLIED MATERIALS ISRAEL LTD.

    Inventor： Ohad SHAUBI ,  Boaz COHEN ,  Kirill SAVCHENKO ,  Ore SHTALRID

    IPC: G06T7/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776

    Abstract: There is provided a method of automated defects' classification, and a system thereof. The method comprises obtaining data informative of a set of defects' physical attributes usable to distinguish between defects of different classes among the plurality of classes; training a first machine learning model to generate, for the given defect, a multi-label output vector informative of values of the physical attributes, thereby generating for the given defect a multi-label descriptor; and using the trained first machine learning model to generate multi-label descriptors of the defects in the specimen. The method can further comprise obtaining data informative of multi-label data sets, each data set being uniquely indicative of a respective class of the plurality of classes and comprising a unique set of values of the physical attributes; and classifying defects in the specimen by matching respectively generated multi-label descriptors of the defects to the multi-label data sets.

    METHOD OF DEEP LEARNING-BASED EXAMINATION OF A SEMICONDUCTOR SPECIMEN AND SYSTEM THEREOF
    14.
    发明申请
    METHOD OF DEEP LEARNING-BASED EXAMINATION OF A SEMICONDUCTOR SPECIMEN AND SYSTEM THEREOF 审中-公开

    公开(公告)号：US20200294224A1

    公开(公告)日：2020-09-17

    申请号：US16892123

    申请日：2020-06-03

    Applicant: APPLIED MATERIALS ISRAEL LTD.

    Inventor： Ohad SHAUBI ,  Denis SUHANOV ,  Assaf ASBAG ,  Boaz COHEN

    IPC: G06T7/00 ,  G06K9/62

    Abstract: There is provided a method of examination of a semiconductor specimen and a system thereof. The method comprises: using a trained Deep Neural Network (DNN) to process a fabrication process (FP) sample, wherein the FP sample comprises first FP image(s) received from first examination modality(s) and second FP image(s) received from second examination modality(s) which differs from the first examination modality(s), and wherein the trained DNN processes the first FP image(s) separately from the second FP image(s); and further processing by the trained DNN the results of such separate processing to obtain examination-related data specific for the given application and characterizing at least one of the processed FP images. When the FP sample further comprises numeric data associated with the FP image(s), the method further comprises processing by the trained DNN at least part of the numeric data separately from processing the first and the second FP images.

    MASK INSPECTION OF A SEMICONDUCTOR SPECIMEN
    16.
    发明申请
    MASK INSPECTION OF A SEMICONDUCTOR SPECIMEN 有权

    公开(公告)号：US20220254000A1

    公开(公告)日：2022-08-11

    申请号：US17730117

    申请日：2022-04-26

    Applicant: Applied Materials Israel Ltd.

    Inventor： Ariel SHKALIM ,  Vladimir OVECHKIN ,  Evgeny BAL ,  Ronen MADMON ,  Ori PETEL ,  Alexander CHERESHNYA ,  Oren Shmuel COHEN ,  Boaz COHEN

    IPC: G06T7/00 ,  G06T1/00 ,  G03F7/20

    Abstract: There is provided a mask inspection system and a method of mask inspection. The method comprises: detecting, by the inspection tool, a runtime defect at a defect location on a mask of a semiconductor specimen during runtime scan of the mask, and acquiring, by the inspection tool after runtime and based on the defect location, a plurality sets of aerial images of the runtime defect corresponding to a plurality of focus states throughout a focus process window, each set of aerial images acquired at a respective focus state. The method further comprises for each set of aerial images, calculating a statistic-based EPD value of the runtime defect, thereby giving rise to a plurality of statistic-based EPD values each corresponding to a respective focus state, and determining whether the runtime defect is a true defect based on the plurality of statistic-based EPD values.

    DETERMINATION OF DEFECT LOCATION FOR EXAMINATION OF A SPECIMEN
    17.
    发明申请
    DETERMINATION OF DEFECT LOCATION FOR EXAMINATION OF A SPECIMEN 有权

    公开(公告)号：US20210256687A1

    公开(公告)日：2021-08-19

    申请号：US16794172

    申请日：2020-02-18

    Applicant: Applied Materials Israel Ltd.

    Inventor： Doron GIRMONSKY ,  Rafael BEN AMI ,  Boaz COHEN ,  Dror SHEMESH

    IPC: G06T7/00 ,  G06F17/18

    Abstract: There is provided a method and a system configured to obtain an image of a one or more first areas of a semiconductor specimen acquired by an examination tool, determine data Datt informative of defectivity in the one or more first areas, determine one or more second areas of the semiconductor specimen for which presence of a defect is suspected based at least on an evolution of Datt, or of data correlated to Datt, in the one or more first areas, and select the one or more second areas for inspection by the examination tool.

    MACHINE LEARNING-BASED DEFECT DETECTION OF A SPECIMEN
    18.
    发明申请
    MACHINE LEARNING-BASED DEFECT DETECTION OF A SPECIMEN 有权

    公开(公告)号：US20210209418A1

    公开(公告)日：2021-07-08

    申请号：US16733219

    申请日：2020-01-02

    Applicant: Applied Materials Israel Ltd.

    Inventor： Ran BADANES ,  Ran SCHLEYEN ,  Boaz COHEN ,  Irad PELEG ,  Denis SUHANOV ,  Ore SHTALRID

    IPC: G06K9/62 ,  G06T7/00

    Abstract: 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.

    METHOD OF DEEP LEARNING-BASED EXAMINATION OF A SEMICONDUCTOR SPECIMEN AND SYSTEM THEREOF
    20.
    发明申请
    METHOD OF DEEP LEARNING-BASED EXAMINATION OF A SEMICONDUCTOR SPECIMEN AND SYSTEM THEREOF 审中-公开

    公开(公告)号：US20170364798A1

    公开(公告)日：2017-12-21

    申请号：US15675477

    申请日：2017-08-11

    Applicant: Applied Materials Israel Ltd.

    Inventor： Leonid KARLINSKY ,  Boaz COHEN ,  Idan KAIZERMAN ,  Efrat ROSENMAN ,  Amit BATIKOFF ,  Daniel RAVID ,  Moshe ROSENWEIG

    IPC: G06N3/08

    CPC classification number: G06N3/08 ,  G06K9/036 ,  G06K9/4628 ,  G06K9/6271 ,  G06N3/0454

    Abstract: There are provided system and method of classifying defects in a semiconductor specimen. The method comprises: upon obtaining by a computer a Deep Neural Network (DNN) trained to provide classification-related attributes enabling minimal defect classification error, processing a fabrication process (FP) sample using the obtained trained DNN; and, resulting from the processing, obtaining by the computer classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification, in accordance with the obtained classification-related attributes, of the at least one defect presented in the FP image. The DNN is trained using a classification training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprising a training image presenting at least one defect and the ground truth data is informative of classes and/or class distribution of defects presented in the respective first training samples; the FP sample comprises a FP image presenting at least one defect to be classified.

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