METHOD FOR TRAINING MACHINE LEARNING MODEL FOR IMPROVING PATTERNING PROCESS

    公开(公告)号:US20220284344A1

    公开(公告)日:2022-09-08

    申请号:US17631557

    申请日:2020-07-30

    Abstract: A method for training a machine learning model configured to predict values of a physical characteristic associated with a substrate and for use in adjusting a patterning process. The method involves obtaining a reference image; determining a first set of model parameter values of the machine learning model such that a first cost function is reduced from an initial value of the cost function obtained using an initial set of model parameter values, where the first cost function is a difference between the reference image and an image generated via the machine learning model; and training, using the first set of model parameter values, the machine learning model such that a combination of the first cost function and a second cost function is iteratively reduced, the second cost function representing a difference between measured values and predicted values.

    METHOD FOR DETERMINING PATTERN IN A PATTERNING PROCESS

    公开(公告)号:US20220179321A1

    公开(公告)日:2022-06-09

    申请号:US17442662

    申请日:2020-03-05

    Abstract: A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern is reduced.

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