Detecting outliers at a manufacturing system using machine learning

    公开(公告)号:US11842910B2

    公开(公告)日:2023-12-12

    申请号:US17168041

    申请日:2021-02-04

    CPC classification number: H01L21/67288 G06N5/04 G06N20/00 H01L22/12

    Abstract: Methods and systems for detecting outliers at a manufacturing system using machine learning are provided. Data collected by a sensors at a manufacturing system during a current process performed for a first set of substrates is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. A first amount of drift of a first set of parameter values for the first set of substrates from a target set of parameter values for the first set of substrates is extracted from the one or more outputs. A second amount of drift of each of the first set of parameter values for the first set of substrates from a corresponding parameter value of a second set of parameter values for a second set of substrates processed according to the current process at the manufacturing system prior to the performance of the current process for the first set of substrates is also extracted from the one or more outputs. A substrate health rating is assigned for each of the first set of substrates based on the first amount of drift. A sensor health rating is assigned for each of the sensors at the manufacturing system based on the second amount of drift. An indication of the substrate health rating for each of the first set of substrates and the sensor health rating for each of the sensors are transmitted to a client device connected to the manufacturing system.

    PIECEWISE FUNCTIONAL FITTING OF SUBSTRATE PROFILES FOR PROCESS LEARNING

    公开(公告)号:US20240054333A1

    公开(公告)日:2024-02-15

    申请号:US17884462

    申请日:2022-08-09

    CPC classification number: G06N3/08

    Abstract: A method includes receiving, by a processing device, data indicative of a plurality of measurements of a profile of a substrate. The method further includes separating the data into a plurality of sets of data, a first set of the plurality of sets associated with a first region of the profile, and a second set of the plurality of sets associated with a second region of the profile. The method further includes fitting data of the first set to a first function to generate a first fit function. The first function is selected from a library of functions. The method further includes fitting data of the second set to a second function to generate a second fit function. The method further includes generating a piecewise functional fit of the profile of the substrate. The piecewise functional fit includes the first fit function and the second fit function.

    DETECTING OUTLIERS AT A MANUFACTURING SYSTEM USING MACHINE LEARNING

    公开(公告)号:US20220246457A1

    公开(公告)日:2022-08-04

    申请号:US17168041

    申请日:2021-02-04

    Abstract: Methods and systems for detecting outliers at a manufacturing system using machine learning are provided. Data collected by a sensors at a manufacturing system during a current process performed for a first set of substrates is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. A first amount of drift of a first set of parameter values for the first set of substrates from a target set of parameter values for the first set of substrates is extracted from the one or more outputs. A second amount of drift of each of the first set of parameter values for the first set of substrates from a corresponding parameter value of a second set of parameter values for a second set of substrates processed according to the current process at the manufacturing system prior to the performance of the current process for the first set of substrates is also extracted from the one or more outputs. A substrate health rating is assigned for each of the first set of substrates based on the first amount of drift. A sensor health rating is assigned for each of the sensors at the manufacturing system based on the second amount of drift. An indication of the substrate health rating for each of the first set of substrates and the sensor health rating for each of the sensors are transmitted to a client device connected to the manufacturing system.

    INTERACTIVE DATA LABELING FOR SUBSTRATE GENERATION PROCESSES

    公开(公告)号:US20250021832A1

    公开(公告)日:2025-01-16

    申请号:US18221301

    申请日:2023-07-12

    Abstract: A method includes obtaining, by a processing device, first data indicative of substrate generation parameters of a first substrate. The processing device further obtains second data indicative of properties of the first substrate. The processing device further obtains third data indicative of substrate generation parameters of a second substrate. The processing device further receives fourth data indicative of properties of the second substrate. The method further includes providing a user interface (UI). The UI includes a first UI element for presenting a visual depiction of the second data and a second UI element for presenting a visual depiction of the fourth data. The method further includes receiving user input of a classification of the first substrate and the second substrate. The method further includes performing analysis relating the first data and the third data to the user classifications. The method further includes performing a corrective action based on the analysis.

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