中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

7 records (took 0.011 seconds)

    PROCESSING CHAMBER CALIBRATION
    1.
    发明公开
    PROCESSING CHAMBER CALIBRATION 审中-公开

    公开(公告)号：US20230222264A1

    公开(公告)日：2023-07-13

    申请号：US17571370

    申请日：2022-01-07

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Rohit Mahakali ,  Elizabeth Kathryn Neville ,  Adolph Miller Allen ,  Xiaoxiong Yuan ,  Weize Hu ,  Karthik Ramanathan

    IPC: G06F30/27 ,  H01L21/67

    CPC classification number: G06F30/27 ,  H01L21/67276 ,  H01L21/67155 ,  H01L21/67248

    Abstract: A method includes receiving, from sensors, sensor data associated with processing a substrate via a processing chamber of substrate processing equipment. The sensor data includes a first subset received from one or more first sensors and a second subset received from one or more second sensors, the first subset being mapped to the second subset. The method further includes identifying model input data and model output data. The model output data is output from a physics-based model based on model input data. The method further includes training a machine learning model with data input including the first subset and the model input data, and target output data including the second subset and the model output data to tune calibration parameters of the machine learning model. The calibration parameters are to be used by the physics-based model to perform corrective actions associated with the processing chamber.

    Reducing substrate surface scratching using machine learning
    3.
    发明授权
    Reducing substrate surface scratching using machine learning 有权

    公开(公告)号：US11586160B2

    公开(公告)日：2023-02-21

    申请号：US17360652

    申请日：2021-06-28

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Kartik B Shah ,  Satish Radhakrishnan ,  Karthik Ramanathan ,  Karthikeyan Balaraman ,  Adolph Miller Allen ,  Xinyuan Chong ,  Mitrabhanu Sahu ,  Wenjing Xu ,  Michael Sterling Jackson ,  Weize Hu ,  Feng Chen

    IPC: H05B3/68 ,  G05B13/02 ,  G05B13/04

    Abstract: Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.

    REDUCING SUBSTRATE SURFACE SCRATCHING USING MACHINE LEARNING
    7.
    发明申请
    REDUCING SUBSTRATE SURFACE SCRATCHING USING MACHINE LEARNING 有权

    公开(公告)号：US20220413452A1

    公开(公告)日：2022-12-29

    申请号：US17360652

    申请日：2021-06-28

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Kartik B. Shah ,  Satish Radhakrishnan ,  Karthik Ramanathan ,  Karthikeyan Balaraman ,  Adolph Miller Allen ,  Xinyuan Chong ,  Mitrabhanu Sahu ,  Wenjing Xu ,  Michael Sterling Jackson ,  Weize Hu ,  Feng Chen

    IPC: G05B13/02 ,  G05B13/04

    Abstract: Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.

Patent Agency Ranking