DETECTING AND CORRECTING SUBSTRATE PROCESS DRIFT USING MACHINE LEARNING

    公开(公告)号:WO2022047235A1

    公开(公告)日:2022-03-03

    申请号:PCT/US2021/048061

    申请日:2021-08-27

    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.

    INTEGRATED SUBSTRATE MEASUREMENT SYSTEM TO IMPROVE MANUFACTURING PROCESS PERFORMANCE

    公开(公告)号:WO2022020517A1

    公开(公告)日:2022-01-27

    申请号:PCT/US2021/042639

    申请日:2021-07-21

    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.

    SYSTEMS AND METHODS FOR ADAPTIVE TROUBLESHOOTING OF SEMICONDUCTOR MANUFACTURING EQUIPMENT

    公开(公告)号:WO2023028345A1

    公开(公告)日:2023-03-02

    申请号:PCT/US2022/041749

    申请日:2022-08-26

    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.

    DETERMINING SUBSTRATE PROFILE PROPERTIES USING MACHINE LEARNING

    公开(公告)号:WO2022020524A1

    公开(公告)日:2022-01-27

    申请号:PCT/US2021/042646

    申请日:2021-07-21

    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.

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