SELF-SUPERVISED REPRESENTATION LEARNING FOR INTERPRETATION OF OCD DATA

    公开(公告)号:US20240069445A1

    公开(公告)日:2024-02-29

    申请号:US18241923

    申请日:2023-09-04

    Applicant: NOVA LTD

    CPC classification number: G03F7/70625 G03F7/70508 G06N3/08

    Abstract: A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

    SELF-SUPERVISED REPRESENTATION LEARNING FOR INTERPRETATION OF OCD DATA

    公开(公告)号:US20230014976A1

    公开(公告)日:2023-01-19

    申请号:US17790765

    申请日:2021-01-06

    Applicant: NOVA LTD

    Abstract: A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

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