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    Process development visualization tool
    24.
    发明授权
    Process development visualization tool 有权

    公开(公告)号：US11157661B2

    公开(公告)日：2021-10-26

    申请号：US16716274

    申请日：2019-12-16

    Applicant: Applied Materials, Inc.

    Inventor： Vinayak Veer Vats ,  Sidharth Bhatia ,  Garrett Ho-Yee Sin ,  Pramod Nambiar ,  Hang Yu ,  Sanjay Kamath ,  Deenesh Padhi ,  Heng-Cheng Pai

    IPC: G06F30/12 ,  G06F16/904 ,  G06F16/903 ,  G06F119/18

    Abstract: A process development visualization tool generates a first visualization of a parameter associated with a manufacturing process, and provides a GUI control element associated with a process variable of the manufacturing process, wherein the GUI control element has a first setting associated with a first value for the process variable. The process development tool receives a user input to adjust the GUI control element from the first setting to a second setting, determines a second value for the process variable based on the second setting, and determines a second set of values for the parameter that are associated with the second value for the process variable. The process development tool then generates a second visualization of the parameter, wherein the second visualization represents the second set of values for the parameter that are associated with the second value for the process variable.

    Apparatus and methods for removing contaminant particles in a plasma process
    25.
    发明授权
    Apparatus and methods for removing contaminant particles in a plasma process 有权

    公开(公告)号：US11120976B2

    公开(公告)日：2021-09-14

    申请号：US16927618

    申请日：2020-07-13

    Applicant: Applied Materials, Inc.

    Inventor： Bhaskar Kumar ,  Anup Kumar Singh ,  Vivek Bharat Shah ,  Sidharth Bhatia ,  Ganesh Balasubramanian

    IPC: H01J37/32 ,  H01L21/67

    Abstract: A method and apparatus for operating a plasma processing chamber includes performing a plasma process at a process pressure and a pressure power to generate a plasma. A first ramping-down stage starts in which the process power and the process pressure are ramped down substantially simultaneously to an intermediate power level and an intermediate pressure level, respectively. The intermediate power level and intermediate pressure level are preselected so as to raise a plasma sheath boundary above a threshold height from a surface of a substrate. A purge gas is flowed from a showerhead assembly at a sufficiently high rate to sweep away contaminant particles trapped in the plasma such that one or more contaminant particles move outwardly of an edge of the substrate. A second ramping-down stage starts where the intermediate power level and the intermediate pressure level decline to a zero level and a base pressure, respectively.

    SENSOR METROLOGY DATA INTERGRATION
    27.
    发明申请
    SENSOR METROLOGY DATA INTERGRATION 审中-公开

    公开(公告)号：US20200264335A1

    公开(公告)日：2020-08-20

    申请号：US16791081

    申请日：2020-02-14

    Applicant: Applied Materials, Inc.

    Inventor： Sidharth Bhatia ,  Garrett H. Sin ,  Heng-Cheng Pai ,  Pramod Nambiar ,  Ganesh Balasubramanian ,  Irfan Jamil

    IPC: G01W1/10 ,  G01W1/02 ,  G06N20/00

    Abstract: Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

    DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING
    29.
    发明公开
    DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING 审中-公开

    公开(公告)号：US20240176312A1

    公开(公告)日：2024-05-30

    申请号：US18070448

    申请日：2022-11-28

    Applicant: Applied Materials, Inc.

    Inventor： Sidharth Bhatia ,  Roger Lindley ,  Upendra Ummethala ,  Thomas Li ,  Michael Howells ,  Steven Babayan ,  Mimi-Diemmy Dao

    IPC: G05B13/02 ,  G05B13/04

    CPC classification number: G05B13/027 ,  G05B13/048

    Abstract: A method includes providing, as input to a first trained machine learning model, trace data associated with one or more substrate processing procedures. The input further includes equipment constants associated with the one or more substrate processing procedures. The input further includes trace data of a first processing chamber. The input further includes equipment constants of the first processing chamber. The method further includes obtaining, as output from the first trained machine learning model, a recommended update to a first equipment constant of the first processing chamber. The method further includes updated the first equipment constant of the first processing chamber responsive to obtaining the output from the first trained machine learning model.

    PREDICTIVE MODELING OF A MANUFACTURING PROCESS USING A SET OF TRAINED INVERTED MODELS
    30.
    发明公开
    PREDICTIVE MODELING OF A MANUFACTURING PROCESS USING A SET OF TRAINED INVERTED MODELS 审中-公开

    公开(公告)号：US20240046096A1

    公开(公告)日：2024-02-08

    申请号：US18489528

    申请日：2023-10-18

    Applicant: Applied Materials, Inc.

    Inventor： Sidharth Bhatia ,  Dermot Cantwell ,  Serghei Malkov ,  Jie Feng

    IPC: G06N3/08 ,  G05B19/418 ,  G05B13/02 ,  G06N3/045

    CPC classification number: G06N3/08 ,  G05B19/41885 ,  G05B13/027 ,  G06N3/045 ,  G05B2219/32335

    Abstract: Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data defining an attribute of a semiconductor device manufactured by at least one semiconductor device manufacturing process performed within at least one processing chamber, wherein the expected output data corresponds to an unexplored portion of a process space associated with the at least one semiconductor device manufacturing process, and identifying expected input data by using the expected output data as input to a plurality of homogeneous inverted machine learning models, wherein each inverted machine learning model of the plurality of homogeneous inverted machine learning models is trained to determine, by performing linear extrapolation based on the expected output data, a respective set of input data of a plurality of sets of input data for configuring the semiconductor device manufacturing process to manufacture the semiconductor device.

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