PATTERN PLACEMENT ERROR AWARE OPTIMIZATION
    1.
    发明申请

    公开(公告)号:US20170082927A1

    公开(公告)日:2017-03-23

    申请号:US15126234

    申请日:2015-03-03

    Abstract: A method to improve a lithographic process for imaging a portion of a design layout onto a substrate using a lithographic projection apparatus, the method including: computing a multi-variable cost function of a plurality of design variables that are characteristics of the lithographic process, and reconfiguring the characteristics of the lithographic process by adjusting the design variables until a predefined termination condition is satisfied. The multi-variable cost function may be a function of one or more pattern shift errors. Reconfiguration of the characteristics may be under one or more constraints on the one or more pattern shift errors.

    METROLOGY USING A PLURALITY OF METROLOGY TARGET MEASUREMENT RECIPES

    公开(公告)号:US20210208512A1

    公开(公告)日:2021-07-08

    申请号:US16346135

    申请日:2017-11-01

    Abstract: A method of measuring a parameter of a patterning process, the method including obtaining a measurement of a substrate processed by a patterning process, with a first metrology target measurement recipe; obtaining a measurement of the substrate with a second, different metrology target measurement recipe, wherein measurements using the first and second metrology target measurement recipes have their own distinct sensitivity to a metrology target structural asymmetry of the patterning process; and determining a value of the parameter by a weighted combination of the measurements of the substrate using the first and second metrology target measurement recipes, wherein the weighting reduces or eliminates the effect of the metrology target structural geometric asymmetry on the parameter of the patterning process determined from the measurements using the first and second metrology target measurement recipes.

    METHODS AND SYSTEMS FOR PATTERN DESIGN WITH TAILORED RESPONSE TO WAVEFRONT ABERRATION
    6.
    发明申请
    METHODS AND SYSTEMS FOR PATTERN DESIGN WITH TAILORED RESPONSE TO WAVEFRONT ABERRATION 有权
    图形设计的方法和系统,具有针对波形排除的定制响应

    公开(公告)号:US20150153651A1

    公开(公告)日:2015-06-04

    申请号:US14575609

    申请日:2014-12-18

    Abstract: The present invention relates to methods and systems for designing gauge patterns that are extremely sensitive to parameter variation, and thus robust against random and repetitive measurement errors in calibration of a lithographic process utilized to image a target design having a plurality of features. The method may include identifying most sensitive line width/pitch combination with optimal assist feature placement which leads to most sensitive CD (or other lithography response parameter) changes against lithography process parameter variations, such as wavefront aberration parameter variation. The method may also include designing gauges which have more than one test patterns, such that a combined response of the gauge can be tailored to generate a certain response to wavefront-related or other lithographic process parameters. The sensitivity against parameter variation leads to robust performance against random measurement error and/or any other measurement error.

    Abstract translation: 本发明涉及用于设计对参数变化非常敏感的规格图案的方法和系统,因此对于用于对具有多个特征进行成像的目标设计的光刻工艺的校准中的随机和重复的测量误差是鲁棒的。 该方法可以包括以最佳辅助特征放置来识别最敏感的线宽/间距组合,其导致针对光刻过程参数变化(例如波前像差参数变化)的最敏感的CD(或其他光刻响应参数)变化。 该方法还可以包括设计具有多于一个测试图案的计量器,使得量规的组合响应可以被调整以产生对波前相关或其它光刻工艺参数的特定响应。 对参数变化的敏感性导致对随机测量误差和/或任何其他测量误差的鲁棒性能。

    APPARATUS AND METHOD FOR GROUPING IMAGE PATTERNS TO DETERMINE WAFER BEHAVIOR IN A PATTERNING PROCESS

    公开(公告)号:US20220028052A1

    公开(公告)日:2022-01-27

    申请号:US17311422

    申请日:2019-11-12

    Abstract: Grouping image patterns to determine wafer behavior in a patterning process with a trained machine learning model is described. The described operations include converting, based on the trained machine learning model, one or more patterning process images including the image patterns into feature vectors. The feature vectors correspond to the image patterns. The described operations include grouping, based on the trained machine learning model, feature vectors with features indicative of image patterns that cause matching wafer and/or wafer defect behavior in the patterning process. The one or more patterning process images include aerial images, resist images, and/or other images. The grouped feature vectors may be used to: detect potential patterning defects on a wafer during a lithography manufacturability check as part of optical proximity correction, adjust a mask layout design, and/or generate a gauge line/defect candidate list, among other uses.

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