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

    公开(公告)号:US20240420025A1

    公开(公告)日:2024-12-19

    申请号:US18703486

    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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