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    DETECTING AND CORRECTING SUBSTRATE PROCESS DRIFT USING MACHINE LEARNING
    2.
    发明申请
    DETECTING AND CORRECTING SUBSTRATE PROCESS DRIFT USING MACHINE LEARNING 有权

    公开(公告)号：US20220066411A1

    公开(公告)日：2022-03-03

    申请号：US17379728

    申请日：2021-07-19

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Upendra V. Ummethala ,  Blake Erickson ,  Prashanth Kumar ,  Michael Kutney ,  Steven Trey Tindel ,  Zhaozhao Zhu

    IPC: G05B19/401 ,  G06N20/00 ,  G06N5/04

    Abstract: Methods and systems for detecting and correcting substrate process drift using machine learning are provided. Data associated with processing each of a first set of substrates at a manufacturing system according to a process recipe is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. An amount of drift of a first set of metrology measurement values for the first set of substrates from a target metrology measurement value is determined from the one or more outputs. Process recipe modification identifying one or more modifications to the process recipe is also determined. For each modification, an indication of a level of confidence that a respective modification to the process recipe satisfies a drift criterion for a second set of substrates is determined. In response to an identification of the respective modification with a level of confidence that satisfies a level of confidence criterion, the process recipe is updated based on the respective modification.

    DETERMINING SUBSTRATE PROFILE PROPERTIES USING MACHINE LEARNING
    3.
    发明申请
    DETERMINING SUBSTRATE PROFILE PROPERTIES USING MACHINE LEARNING 有权

    公开(公告)号：US20230062206A1

    公开(公告)日：2023-03-02

    申请号：US18046872

    申请日：2022-10-14

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Thomas Li ,  Upendra V. Ummethala ,  Blake Erickson ,  Prashanth Kumar ,  Michael Kutney ,  Steven Trey Tindel ,  Zhaozhao Zhu

    IPC: G03F7/20

    Abstract: Spectral data associated with a first prior substrate and/or a second prior substrate is obtained. A metrology measurement value associated with the first portion of the first prior substrate is determined based on one or more metrology measurement values measured for at least one of a second portion of the first prior substrate or a third portion of a second prior substrate. Training data for training a machine learning model to predict metrology measurement values of a current substrate is generated. Generating the training data includes generating a first training input including the spectral data associated with the first prior substrate and generating a first target output for the first training input, the first target output including the determined metrology measurement value associated with the first portion of the first prior substrate. The training data is provided to train the machine learning model.

    Substrate measurement subsystem
    4.
    发明授权
    Substrate measurement subsystem 有权

    公开(公告)号：US12283503B2

    公开(公告)日：2025-04-22

    申请号：US17379677

    申请日：2021-07-19

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Upendra V. Ummethala ,  Blake Erickson ,  Prashanth Kumar ,  Michael Kutney ,  Steven Trey Tindel ,  Zhaozhao Zhu

    IPC: G01N27/07 ,  G01B11/00 ,  G01N21/95 ,  H01L21/66 ,  H01L21/67 ,  G01N21/27

    Abstract: A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

    Integrated substrate measurement system to improve manufacturing process performance
    6.
    发明授权
    Integrated substrate measurement system to improve manufacturing process performance 有权

    公开(公告)号：US11688616B2

    公开(公告)日：2023-06-27

    申请号：US17379653

    申请日：2021-07-19

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Upendra V. Ummethala ,  Blake Erickson ,  Prashanth Kumar ,  Michael Kutney ,  Steven Trey Tindel ,  Zhaozhao Zhu

    IPC: H01L21/67 ,  H01L21/66

    CPC classification number: H01L21/67253 ,  H01L21/67288 ,  H01L22/20 ,  H01L21/67063 ,  H01L21/67167

    Abstract: A method for determining whether to modify a manufacturing process recipe is provided. A substrate to be processed at a manufacturing system according to the first process recipe is identified. An instruction to transfer the substrate to a substrate measurement subsystem to obtain a first set of measurements for the substrate is generated. The first set of measurements for the substrate is received from the substrate measurement subsystem. An instruction to transfer the substrate from the substrate measurement subsystem to a processing chamber is generated. A second set of measurements for the substrate is received from one or more sensors of the processing chamber. A first mapping between the first set of measurements and the second set of measurements for the substrate is generated. The first set of measurements mapped to the second set of measurements for the substrate is stored. A determination is made based on the first set of measurements mapped to the second set of measurements for the substrate of whether to modify the first process recipe or a second process recipe for the substrate.

    DETERMINING SUBSTRATE PROFILE PROPERTIES USING MACHINE LEARNING
    7.
    发明申请
    DETERMINING SUBSTRATE PROFILE PROPERTIES USING MACHINE LEARNING 有权

    公开(公告)号：US20220026817A1

    公开(公告)日：2022-01-27

    申请号：US17379707

    申请日：2021-07-19

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Upendra V. Ummethala ,  Blake Erickson ,  Prashanth Kumar ,  Michael Kutney ,  Steven Trey Tindel ,  Zhaozhao Zhu

    IPC: G03F7/20 ,  G06N20/00

    Abstract: A method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system is provided. Training data for the machine learning model is generated. A first training input including historical spectral data and/or historical non-spectral data associated with a surface of a prior substrate previously processed at the manufacturing system is generated. A first target output for the first training input is generated. The first target output includes historical metrology measurements associated with the prior substrate previously processed at the manufacturing system. Data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including a first target output.

    SYSTEMS AND METHODS FOR ADAPTIVE TROUBLESHOOTING OF SEMICONDUCTOR MANUFACTURING EQUIPMENT
    10.
    发明申请
    SYSTEMS AND METHODS FOR ADAPTIVE TROUBLESHOOTING OF SEMICONDUCTOR MANUFACTURING EQUIPMENT 有权

    公开(公告)号：US20230061513A1

    公开(公告)日：2023-03-02

    申请号：US17459433

    申请日：2021-08-27

    Applicant: APPLIED MATERIALS, INC.

    Inventor： Milind Jayram Gadre ,  Prashanth Kumar

    IPC: G05B19/406 ,  G06N20/00 ,  G06F16/901

    Abstract: A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate; generating a manufacturing data graph based on the plurality of sensor values; receiving, via a user interface, a selection of a data point on the manufacturing graph; receiving failure data associated with the data point; and storing, in a data structure, the failure data to be accessible via the user interface presenting the manufacturing data graph.

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