Lithographic Apparatus Adjustment Method
    2.
    发明申请

    公开(公告)号:US20200272059A1

    公开(公告)日:2020-08-27

    申请号:US16644135

    申请日:2018-08-14

    Abstract: A method comprising determining aberrations caused by each lithographic apparatus of a set of lithographic apparatuses, calculating adjustments of the lithographic apparatuses which minimize differences between the aberrations caused by each of the lithographic apparatuses, and applying the adjustments to the lithographic apparatuses, providing better matching between the aberrations of patterns projected by the lithographic apparatuses.

    METHODS OF TUNING A MODEL FOR A LITHOGRAPHIC PROCESS AND ASSOCIATED APPARATUSES

    公开(公告)号:US20230084130A1

    公开(公告)日:2023-03-16

    申请号:US17795058

    申请日:2021-01-11

    Abstract: A method at tuning a lithographic process for a particular patterning device. The method includes: obtaining wavefront data relating to an objective lens of a lithographic apparatus, the wavefront data measured subsequent to an exposure of a pattern on a substrate using the particular patterning device; determining a pattern specific wavefront contribution from the wavefront data and a wavefront reference, the pattern specific wavefront contribution relating to the patterning device; and tuning the lithographic process for the particular patterning device using the pattern specific wavefront contribution.

    Projection System Modelling Method
    4.
    发明申请

    公开(公告)号:US20190227441A1

    公开(公告)日:2019-07-25

    申请号:US16307372

    申请日:2017-05-15

    Abstract: A projection system model is configured to predict optical aberrations of a projection system based upon a set of projection system characteristics and to determine and output a set of optical element adjustments based upon a merit function. The merit function comprises a set of parameters and corresponding weights. The method comprises receiving an initial merit function and executing an optimization algorithm to determine a second merit function. The optimization algorithm scores different merit functions based upon projection system characteristics of a projection system adjusted according to the output of the projection system model using a merit function having that set of parameters and weights.

    METHODS FOR SAMPLE SCHEME GENERATION AND OPTIMIZATION

    公开(公告)号:US20240046022A1

    公开(公告)日:2024-02-08

    申请号:US18239814

    申请日:2023-08-30

    Inventor: Pierluigi FRISCO

    CPC classification number: G06F30/398 G03F7/705 G06F2111/06

    Abstract: A method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. A method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. The locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.

    METHODS FOR SAMPLE SCHEME GENERATION AND OPTIMIZATION

    公开(公告)号:US20220057716A1

    公开(公告)日:2022-02-24

    申请号:US17311846

    申请日:2019-12-12

    Inventor: Pierluigi FRISCO

    Abstract: A method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. A method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. The locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.

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