Metrology Method and Apparatus, Lithographic System and Device Manufacturing Method

    公开(公告)号:US20190278190A1

    公开(公告)日:2019-09-12

    申请号:US16421697

    申请日:2019-05-24

    Abstract: Disclosed is a method of measuring a parameter of a lithographic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.

    Metrology Method and Apparatus, Lithographic System and Device Manufacturing Method
    4.
    发明申请
    Metrology Method and Apparatus, Lithographic System and Device Manufacturing Method 有权
    计量方法与仪器,平版印刷系统和器件制造方法

    公开(公告)号:US20160161864A1

    公开(公告)日:2016-06-09

    申请号:US14906896

    申请日:2014-07-18

    CPC classification number: G03F7/70633

    Abstract: Disclosed is a method of measuring a parameter of a litho-graphic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.

    Abstract translation: 公开了一种测量光刻图形处理参数的方法和相关联的检查装置。 该方法包括使用多个不同的照明条件测量衬底上的至少两个目标结构,所述目标结构具有有意的覆盖偏移; 为每个目标结构获得表示总体不对称性的不对称测量,其包括由于(i)故意重叠偏差引起的贡献,(ii)在形成目标结构期间的覆盖误差,以及(iii)任何特征不对称性。 对不对称测量数据进行回归分析,通过将线性回归模型拟合到针对另一目标结构的不对称测量的一个目标结构的不对称测量的平面表示,线性回归模型不一定通过平面表示的原点拟合 。 然后可以从线性回归模型描述的梯度确定覆盖误差。

    Metrology Method and Apparatus, Lithographic System and Device Manufacturing Method

    公开(公告)号:US20190049860A1

    公开(公告)日:2019-02-14

    申请号:US16159884

    申请日:2018-10-15

    CPC classification number: G03F7/70633

    Abstract: Disclosed is a method of measuring a parameter of a lithographic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.

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