METHOD FOR DETERMINING PATTERN IN A PATTERNING PROCESS

    公开(公告)号:WO2020193095A1

    公开(公告)日:2020-10-01

    申请号:PCT/EP2020/055785

    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 on a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model comprising a first set of parameters, and a machine learning model comprising 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 of the patterning process model is reduced.

    GENERATING AUGMENTED DATA TO TRAIN MACHINE LEARNING MODELS TO PRESERVE PHYSICAL TRENDS

    公开(公告)号:WO2023084063A1

    公开(公告)日:2023-05-19

    申请号:PCT/EP2022/081686

    申请日:2022-11-12

    Abstract: Machine learning models can be trained to predict imaging characteristics with respect to variation in a pattern on a wafer resulting from a patterning process. However, due to low pattern coverage provided by limited wafer data used for training, machine learning models tend to overfit, and predictions from the machine learning models deviate from physical trends that characterize the pattern on the wafer and/or the patterning process with respect to the pattern variation. To enhance pattern coverage, training data is augmented with pattern data that conforms to a certain expected physical trend, and applies to new patterns not covered by previously measured wafer data.

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