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    A MACHINE LEARNING MODEL USING TARGET PATTERN AND REFERENCE LAYER PATTERN TO DETERMINE OPTICAL PROXIMITY CORRECTION FOR MASK
    1.
    发明申请
    A MACHINE LEARNING MODEL USING TARGET PATTERN AND REFERENCE LAYER PATTERN TO DETERMINE OPTICAL PROXIMITY CORRECTION FOR MASK 审中-公开

    公开(公告)号：WO2022179802A1

    公开(公告)日：2022-09-01

    申请号：PCT/EP2022/052213

    申请日：2022-01-31

    Applicant: ASML NETHERLANDS B.V.

    Inventor： ZHANG, Quan ,  CHEN, Been-Der ,  FONG, Wei-chun ,  ZHU, Zhangnan ,  BOONE, Robert, Elliott

    IPC: G03F1/36 ,  G03F7/20

    Abstract: Described are embodiments for generating a post-optical proximity correction (OPC) result for a mask using a target pattern and reference layer patterns. Images of the target pattern and reference layers are provided as an input to a machine learning (ML) model to generate a post-OPC image. The images may be input separately or combined into a composite image (e.g., using a linear function) and input to the ML model. The images are rendered from pattern data. For example, a target pattern image is rendered from a target pattern to be printed on a substrate, and a reference layer image such as dummy pattern image is rendered from dummy pattern. The ML model is trained to generate the post-OPC image using multiple images associated with target patterns and reference layers, and using a reference post-OPC image of the target pattern. The post-OPC image may be used to generate a post-OPC mask.

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