PREDICTION DATA SELECTION FOR MODEL CALIBRATION TO REDUCE MODEL PREDICTION UNCERTAINTY

    公开(公告)号:US20220276563A1

    公开(公告)日:2022-09-01

    申请号:US17625125

    申请日:2020-06-15

    Abstract: Systems and methods for reducing prediction uncertainty in a prediction model associated with a patterning process are described. These may be used in calibrating a process model associated with the patterning process, for example. Reducing the uncertainty in the prediction model may include determining a prediction uncertainty parameter based on prediction data. The prediction data may be determined using the prediction model. The prediction model may have been calibrated with calibration data. The prediction uncertainty parameter may be associated with variation in the prediction data. Reducing the uncertainty in the prediction model may include selecting a subset of process data based on the prediction uncertainty parameter; and recalibrating the prediction model using the calibration data and the selected subset of the process data.

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