Model and parameter selection for optical metrology
    6.
    发明授权
    Model and parameter selection for optical metrology 有权
    光学计量学的模型和参数选择

    公开(公告)号:US07505153B2

    公开(公告)日:2009-03-17

    申请号:US12030166

    申请日:2008-02-12

    摘要: A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. The set of geometric parameters is selected to a set of optimization parameters. The number of optimization parameters within the set of optimization parameters is less than the number of geometric parameters within the set of geometric parameters. A set of selected optimization parameters is selected from the set of optimization parameters. The parameters of the set of selected geometric parameters are used as parameters of the selected profile model. The selected profile model is tested against one or more termination criteria.

    摘要翻译: 选择用于晶片中结构的光学测量的轮廓模型,轮廓模型具有与结构的尺寸相关联的一组几何参数。 选择几组参数集合到一组优化参数中。 该组优化参数内的优化参数的数量小于该组几何参数内的几何参数的数量。 从一组优化参数中选择一组选定的优化参数。 所选择的几何参数集合中的参数被用作所选轮廓模型的参数。 根据一个或多个终止标准测试所选的配置文件模型。

    MODEL AND PARAMETER SELECTION FOR OPTICAL METROLOGY
    7.
    发明申请
    MODEL AND PARAMETER SELECTION FOR OPTICAL METROLOGY 有权
    光学计量学的模型和参数选择

    公开(公告)号:US20080151269A1

    公开(公告)日:2008-06-26

    申请号:US12030166

    申请日:2008-02-12

    IPC分类号: G01B11/14

    摘要: A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. The set of geometric parameters is selected to a set of optimization parameters. The number of optimization parameters within the set of optimization parameters is less than the number of geometric parameters within the set of geometric parameters. A set of selected optimization parameters is selected from the set of optimization parameters. The parameters of the set of selected geometric parameters are used as parameters of the selected profile model. The selected profile model is tested against one or more termination criteria.

    摘要翻译: 选择用于晶片中结构的光学测量的轮廓模型,轮廓模型具有与结构的尺寸相关联的一组几何参数。 选择几组参数集合到一组优化参数中。 该组优化参数内的优化参数的数量小于该组几何参数内的几何参数的数量。 从一组优化参数中选择一组选定的优化参数。 所选择的几何参数集合中的参数被用作所选轮廓模型的参数。 根据一个或多个终止标准测试所选的配置文件模型。

    Model and parameter selection for optical metrology
    8.
    发明授权
    Model and parameter selection for optical metrology 有权
    光学计量学的模型和参数选择

    公开(公告)号:US07330279B2

    公开(公告)日:2008-02-12

    申请号:US10206491

    申请日:2002-07-25

    摘要: A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. A set of optimization parameters is selected for the profile model using one or more input diffraction signals and one or more parameter selection criteria. The selected profile model and the set of optimization parameters are tested against one or more termination criteria. The process of selecting a profile model, selecting a set of optimization parameters, and testing the selected profile model and set of optimization parameters is performed until the one or more termination criteria are met.

    摘要翻译: 选择用于晶片中结构的光学测量的轮廓模型,轮廓模型具有与结构的尺寸相关联的一组几何参数。 使用一个或多个输入衍射信号和一个或多个参数选择标准为轮廓模型选择一组优化参数。 根据一个或多个终止标准测试所选择的简档模型和优化参数集合。 执行选择简档模型,选择一组优化参数以及测试所选简档模型和优化参数的集合的过程,直到满足一个或多个终止标准。

    Optical metrology of structures formed on semiconductor wafers using machine learning systems
    9.
    发明授权
    Optical metrology of structures formed on semiconductor wafers using machine learning systems 有权
    使用机器学习系统在半导体晶圆上形成的结构的光学测量

    公开(公告)号:US07831528B2

    公开(公告)日:2010-11-09

    申请号:US12399011

    申请日:2009-03-05

    CPC分类号: G01B11/24 G06N99/005

    摘要: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

    摘要翻译: 通过获得使用测量装置测量的第一衍射信号来检查形成在半导体晶片上的结构。 使用机器学习系统生成第二衍射信号,其中机器学习系统作为输入接收表征结构的轮廓以产生第二衍射信号的一个或多个参数。 比较第一和第二衍射信号。 当第一和第二衍射信号在匹配标准内匹配时,基于机器学习系统用于生成第二衍射信号的一个或多个参数或轮廓来确定该结构的特征。

    OPTICAL METROLOGY OF STRUCTURES FORMED ON SEMICONDUCTOR WAFERS USING MACHINE LEARNING SYSTEMS
    10.
    发明申请
    OPTICAL METROLOGY OF STRUCTURES FORMED ON SEMICONDUCTOR WAFERS USING MACHINE LEARNING SYSTEMS 有权
    使用机器学习系统在半导体波形上形成的结构的光学计量学

    公开(公告)号:US20090198635A1

    公开(公告)日:2009-08-06

    申请号:US12399011

    申请日:2009-03-05

    IPC分类号: G06F15/18

    CPC分类号: G01B11/24 G06N99/005

    摘要: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

    摘要翻译: 通过获得使用测量装置测量的第一衍射信号来检查形成在半导体晶片上的结构。 使用机器学习系统生成第二衍射信号,其中机器学习系统作为输入接收表征结构的轮廓以产生第二衍射信号的一个或多个参数。 比较第一和第二衍射信号。 当第一和第二衍射信号在匹配标准内匹配时,基于机器学习系统用于生成第二衍射信号的一个或多个参数或轮廓来确定该结构的特征。