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    MACHINE LEARNING MODEL TRAINING
    4.
    发明申请
    MACHINE LEARNING MODEL TRAINING 有权

    公开(公告)号：US20240393262A1

    公开(公告)日：2024-11-28

    申请号：US18201927

    申请日：2023-05-25

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Jeong Jin Hong ,  Sejune Cheon ,  Sang Hong Kim ,  Thomas Ho Fai Li ,  Anders Andelman Nottrott ,  Zhaozhao Zhu ,  MiHyun Jang

    IPC: G01N21/95 ,  G06T7/00

    Abstract: A method includes receiving spectral data of a substrate and metrology data corresponding to the spectral data of the substrate. The method further includes determining a plurality of feature model configurations for each of a plurality of feature models, each of the plurality of feature model configurations including one or more feature model conditions. The method further includes determining a plurality of feature model combinations, where each feature model combination of the plurality of feature model combinations includes a subset of the plurality of feature model configurations. The method further includes generating a plurality of input datasets, where each input dataset of the plurality of input datasets is generated based on application of the spectral data to a respective feature model combination of the plurality of feature model combinations. The method further includes training a plurality of machine learning models, where each machine learning model is trained to generate an output using an input dataset of the plurality of input datasets and the metrology data. The method further includes selecting a trained machine learning model from the plurality of trained machine learning models satisfying one or more selection criteria.

    PREDICTIVE MODELING FOR CHAMBER CONDITION MONITORING
    9.
    发明公开
    PREDICTIVE MODELING FOR CHAMBER CONDITION MONITORING 审中-公开

    公开(公告)号：US20230222394A1

    公开(公告)日：2023-07-13

    申请号：US17571320

    申请日：2022-01-07

    Applicant: Applied Materials, Inc.

    Inventor： Jeong Jin Hong ,  Sejune Cheon

    IPC: G06N20/20 ,  G06K9/62 ,  H01L21/66

    CPC classification number: G06N20/20 ,  G06K9/6247 ,  G06K9/6256 ,  H01L22/12

    Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include a processing device receiving training data. The training data may include first sensor data indicating a first state of an environment of a first processing chamber processing a first substrate. The training data may further include first process tool data indicating a state of first processing tools processing the first substrate. The training data may further include first process result data corresponding to the first substrate processed by the first process tool. The processing device may further train a first model using the training data. The trained first model receives new input having second sensor data and second process tool data to produce second output based on the new input. The second output indicating a second process result data corresponding to a second substrate.

    Diagnostic tool to tool matching and full-trace drill-down analysis methods for manufacturing equipment
    10.
    发明授权
    Diagnostic tool to tool matching and full-trace drill-down analysis methods for manufacturing equipment 有权

    公开(公告)号：US12298748B2

    公开(公告)日：2025-05-13

    申请号：US17586702

    申请日：2022-01-27

    Applicant: Applied Materials, Inc.

    Inventor： Sejune Cheon ,  Jeong Jin Hong ,  Mikyung Shim ,  Xiaoqun Zou ,  Jinkyeong Lee ,  Sang Hong Kim

    IPC: G05B19/418

    Abstract: A method includes receiving trace sensor data associated with a first manufacturing process of a processing chamber. The method further includes processing the trace sensor data using one or more trained machine learning models that generate a representation of the trace sensor data, and then generate reconstructed sensor data based on the representation of the trace sensor data. The method further includes comparing the trace sensor data to the reconstructed sensor data. The method further includes determining one or more differences between the reconstructed sensor data and the trace sensor data. The method further includes determining whether to recommend a corrective action associated with the processing chamber based on the one or more differences between the trace sensor data and the reconstructed sensor data.

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