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    Accelerating semiconductor-related computations using learning based models
    11.
    发明授权
    Accelerating semiconductor-related computations using learning based models 有权

    公开(公告)号：US10360477B2

    公开(公告)日：2019-07-23

    申请号：US15402169

    申请日：2017-01-09

    Applicant: KLA-Tencor Corporation

    Inventor： Kris Bhaskar ,  Scott Young ,  Mark Roulo ,  Jing Zhang ,  Laurent Karsenti ,  Mohan Mahadevan ,  Bjorn Brauer

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/66 ,  G06T7/00

    Abstract: Methods and systems for performing one or more functions for a specimen using output simulated for the specimen are provided. One system includes one or more computer subsystems configured for acquiring output generated for a specimen by one or more detectors included in a tool configured to perform a process on the specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a learning based model configured for performing one or more first functions using the acquired output as input to thereby generate simulated output for the specimen. The one or more computer subsystems are also configured for performing one or more second functions for the specimen using the simulated output.

    Systems and methods incorporating a neural network and a forward physical model for semiconductor applications
    12.
    发明授权
    Systems and methods incorporating a neural network and a forward physical model for semiconductor applications 有权

    公开(公告)号：US10346740B2

    公开(公告)日：2019-07-09

    申请号：US15609009

    申请日：2017-05-31

    Applicant: KLA-Tencor Corporation

    Inventor： Jing Zhang ,  Kris Bhaskar

    IPC: G06N7/02 ,  G06N7/04 ,  G06N7/06 ,  G06N7/08 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62

    Abstract: Methods and systems for training a neural network are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for determining inverted features of input images in a training set for a specimen input to the neural network, a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set, and a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set. The one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network.

    Generating simulated output for a specimen
    13.
    发明授权
    Generating simulated output for a specimen 有权

    公开(公告)号：US10043261B2

    公开(公告)日：2018-08-07

    申请号：US15402094

    申请日：2017-01-09

    Applicant: KLA-Tencor Corporation

    Inventor： Kris Bhaskar ,  Jing Zhang ,  Grace Hsiu-Ling Chen ,  Ashok Kulkarni ,  Laurent Karsenti

    IPC: G06K9/00 ,  G06T7/00 ,  G03F1/84 ,  G03F1/86 ,  G03F7/20

    Abstract: Methods and systems for generating simulated output for a specimen are provided. One method includes acquiring information for a specimen with one or more computer systems. The information includes at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen. The method also includes inputting the information for the specimen into a learning based model. The learning based model is included in one or more components executed by the one or more computer systems. The learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, and the learning based model applies the triangular relationship to the input to thereby generate simulated images for the specimen.

    SYSTEMS AND METHODS INCORPORATING A NEURAL NETWORK AND A FORWARD PHYSICAL MODEL FOR SEMICONDUCTOR APPLICATIONS
    15.
    发明申请
    SYSTEMS AND METHODS INCORPORATING A NEURAL NETWORK AND A FORWARD PHYSICAL MODEL FOR SEMICONDUCTOR APPLICATIONS 审中-公开

    公开(公告)号：US20170351952A1

    公开(公告)日：2017-12-07

    申请号：US15609009

    申请日：2017-05-31

    Applicant: KLA-Tencor Corporation

    Inventor： Jing Zhang ,  Kris Bhaskar

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06K9/6274 ,  G06N3/04 ,  G06N3/0454

    Abstract: Methods and systems for training a neural network are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a neural network configured for determining inverted features of input images in a training set for a specimen input to the neural network, a forward physical model configured for reconstructing the input images from the inverted features thereby generating a set of output images corresponding to the input images in the training set, and a residue layer configured for determining differences between the input images in the training set and their corresponding output images in the set. The one or more computer subsystems are configured for altering one or more parameters of the neural network based on the determined differences thereby training the neural network.

    ACTIVE LEARNING FOR DEFECT CLASSIFIER TRAINING
    18.
    发明申请
    ACTIVE LEARNING FOR DEFECT CLASSIFIER TRAINING 审中-公开

    公开(公告)号：US20190370955A1

    公开(公告)日：2019-12-05

    申请号：US16424431

    申请日：2019-05-28

    Applicant: KLA-Tencor Corporation

    Inventor： Jing Zhang ,  Yujie Dong ,  Brian Duffy ,  Richard Wallingford ,  Michael Daino ,  Kris Bhaskar

    IPC: G06T7/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06N20/00 ,  G06F17/18 ,  G06N7/00

    Abstract: Methods and systems for performing active learning for defect classifiers are provided. One system includes one or more computer subsystems configured for performing active learning for training a defect classifier. The active learning includes applying an acquisition function to data points for the specimen. The acquisition function selects one or more of the data points based on uncertainty estimations associated with the data points. The active learning also includes acquiring labels for the selected one or more data points and generating a set of labeled data that includes the selected one or more data points and the acquired labels. The computer subsystem(s) are also configured for training the defect classifier using the set of labeled data. The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem.

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