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    MACHINE LEARNING MODEL TRAINING
    6.
    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.

    Diagnostic tool to tool matching and full-trace drill-down analysis methods for manufacturing equipment
    8.
    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.

    PROCESS CONTROL KNOB ESTIMATION
    10.
    发明申请
    PROCESS CONTROL KNOB ESTIMATION 有权

    公开(公告)号：US20250104984A1

    公开(公告)日：2025-03-27

    申请号：US18973938

    申请日：2024-12-09

    Applicant: Applied Materials, Inc.

    Inventor： Jeong Jin Hong ,  Sang Hong Kim ,  Mihyun Jang ,  Jin Kyeong Lee ,  Sejune Cheon

    IPC: H01J37/32 ,  G05B23/02 ,  G06N20/20

    Abstract: A non-transitory machine-readable storage medium includes instructions that, when executed by a processing device, cause the processing device to perform operations. The operations include receiving (i) sensor data indicating a first state of a coating of a processing chamber subsequent to a chamber recovery procedure and (ii) substrate process data indicating a set of process parameter values associated with performing a substrate processing procedure by the processing chamber having the coating in a second state prior to the chamber recovery procedure. The operations further include processing the sensor data and the substrate process data to determine an update to at least one of the set of process parameter values. The operations further include performing a) preparing a notification indicating the update for presentation on a graphical user interface or b) causing the processing chamber to perform a selection of the substrate processing procedure based on the update.

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