PROCESS MONITORING AND TUNING USING PREDICTION MODELS

    公开(公告)号:US20220404711A1

    公开(公告)日:2022-12-22

    申请号:US17763698

    申请日:2020-09-22

    Abstract: A method for monitoring performance of a manufacturing process is described. The method includes receiving one or more input signals that convey information related to geometry of a substrate generated by the manufacturing process; and determining, with a prediction model, variation in the manufacturing process based on the one or more input signals. A method for predicting substrate geometry associated with a manufacturing process is also described. The method includes receiving input information including geometry information and manufacturing process information for a substrate; and predicting, using a machine learning prediction model, output substrate geometry based on the input information. The method may further include tuning the predicted output substrate geometry. The tuning includes comparing the output substrate geometry to corresponding physical substrate measurements and/or predictions from a different non-machine learning prediction model, generating a loss function based on the comparison, and optimizing the loss function.

    MACHINE LEARNING MODEL FOR ASYMMETRY-INDUCED OVERLAY ERROR CORRECTION

    公开(公告)号:US20250053097A1

    公开(公告)日:2025-02-13

    申请号:US18716806

    申请日:2022-11-22

    Abstract: A correction to an error of overlay measurement which accounts for target structure asymmetry using a neural network is described. According to embodiments, an overlay measurement accuracy can be improved by accounting for multiple and/or asymmetric perturbations in the target structure. A trained neural network is described which generates a correction value for overlay measurement based on a measure of asymmetry. Based on an as-measured overlay measurement, which may not account for target structure asymmetry, and the correction value, a true overlay measurement is determined-which can exhibit improved accuracy and reduced uncertainty versus uncorrected values.

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