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1. 发明授权

US09607265B2 Accurate and fast neural network training for library-based critical dimension (CD) metrology 有权
公开(公告)号：US09607265B2
公开(公告)日：2017-03-28
申请号：US14044729
申请日：2013-10-02
申请人： Wen Jin , Vi Vuong , Junwei Bao , Lie-Quan Lee , Leonid Poslavsky
发明人： Wen Jin , Vi Vuong , Junwei Bao , Lie-Quan Lee , Leonid Poslavsky
IPC分类号： G06F15/18 , G06N3/08 , G06N3/04
CPC分类号： G06N3/08 , G06N3/0454
摘要： Embodiments are generally directed to neural network training for library-based critical dimension metrology. An embodiment of a method includes optimizing a threshold for a principal component analysis of a spectrum data set to provide a principal component value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the principal component value provided from optimizing the threshold for the principal component analysis, and providing a spectral library based on the one or more trained neural networks.

2. 发明授权

US08577820B2 Accurate and fast neural network training for library-based critical dimension (CD) metrology 有权
标题翻译：基于图书馆的关键维度（CD）计量学的准确快速的神经网络训练
公开(公告)号：US08577820B2
公开(公告)日：2013-11-05
申请号：US13041253
申请日：2011-03-04
申请人： Wen Jin , Vi Vuong , Junwei Bao , Lie-Quan Lee , Leonid Poslavsky
发明人： Wen Jin , Vi Vuong , Junwei Bao , Lie-Quan Lee , Leonid Poslavsky
IPC分类号： G06F15/18
CPC分类号： G06N3/08 , G06N3/0454
摘要： Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described. In an example, a method includes optimizing a threshold for a principal component analysis (PCA) of a spectrum data set to provide a principal component (PC) value, estimating a training target for one or more neural networks, training the one or more neural networks based both on the training target and on the PC value provided from optimizing the threshold for the PCA, and providing a spectral library based on the one or more trained neural networks.
摘要翻译：描述了基于图书馆的关键维度（CD）计量学的准确神经网络训练的方法。还介绍了基于图书馆的CD测量的快速神经网络训练方法。在一个示例中，方法包括优化频谱数据集的主成分分析（PCA）的阈值以提供主成分（PC）值，估计一个或多个神经网络的训练目标，训练一个或多个神经元基于训练目标和通过优化PCA的阈值提供的PC值的网络，以及基于一个或多个训练有素的神经网络提供谱库。

3. 发明申请

US20120226644A1 Accurate and Fast Neural network Training for Library-Based Critical Dimension (CD) Metrology 有权
标题翻译：基于图书馆的关键维度（CD）计量学的精确快速神经网络训练
公开(公告)号：US20120226644A1
公开(公告)日：2012-09-06
申请号：US13041253
申请日：2011-03-04
申请人： Wen Jin , Vi Vuong , Junwei Bao , Lie-Quan Lee , Leonid Poslavsky
发明人： Wen Jin , Vi Vuong , Junwei Bao , Lie-Quan Lee , Leonid Poslavsky
IPC分类号： G06N3/08
CPC分类号： G06N3/08 , G06N3/0454
摘要： Approaches for accurate neural network training for library-based critical dimension (CD) metrology are described. Approaches for fast neural network training for library-based CD metrology are also described.
摘要翻译：描述了基于图书馆的关键维度（CD）计量学的准确神经网络训练的方法。还介绍了基于图书馆的CD测量的快速神经网络训练方法。

4. 发明授权

US07831528B2 Optical metrology of structures formed on semiconductor wafers using machine learning systems 有权
标题翻译：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
申请人： Srinivas Doddi , Emmanuel Drege , Nickhil Jakatdar , Junwei Bao
发明人： Srinivas Doddi , Emmanuel Drege , Nickhil Jakatdar , Junwei Bao
IPC分类号： G06F15/18 , G01R31/26 , G01R31/303
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。

5. 发明授权

US07765234B2 Data flow management in generating different signal formats used in optical metrology 失效
标题翻译：生成用于光学计量学的不同信号格式的数据流管理
公开(公告)号：US07765234B2
公开(公告)日：2010-07-27
申请号：US11580716
申请日：2006-10-12
申请人： Hong Qiu , Junwei Bao , Wei Liu , Jeffrey Alexander Chard , Miao Liu , Gang He , Hemalatha Erva , Vi Vuong
发明人： Hong Qiu , Junwei Bao , Wei Liu , Jeffrey Alexander Chard , Miao Liu , Gang He , Hemalatha Erva , Vi Vuong
IPC分类号： G06Q50/00
CPC分类号： G01N21/4788 , G01B11/24 , Y10S707/953
摘要： To manage data flow in generating different signal formats for use in optical metrology, a project data object is created. A first option data object is created. The first option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. A version number is associated with the first option data object. The first option data object is linked with the project data object. At least a second option data object is created. The second option data object has a set of signal parameters. Different settings of the set of signal parameters correspond to different signal formats for diffraction signals. The set of signal parameters of the first option data object and the set of signal parameters of the second option data object are set differently. Another version number is associated with the second option data object. The second option data object is linked with the project data object. The project data object, the first option data object, and the second option data object are stored. The version numbers associated with the first option data object and the second option data object are stored. The link between the first option data object and the project data object is stored. The link between the second option data object and the project data object is stored.
摘要翻译：为了管理生成用于光学测量的不同信号格式的数据流，创建了一个项目数据对象。创建第一个选项数据对象。第一个选项数据对象具有一组信号参数。信号参数组的不同设置对应于衍射信号的不同信号格式。版本号与第一选项数据对象相关联。第一个选项数据对象与项目数据对象链接。至少创建第二个选项数据对象。第二个选项数据对象具有一组信号参数。信号参数组的不同设置对应于衍射信号的不同信号格式。第一选项数据对象的信号参数集合和第二选项数据对象的信号参数集合设置不同。另一个版本号与第二个选项数据对象相关联。第二个选项数据对象与项目数据对象链接。存储项目数据对象，第一选项数据对象和第二选项数据对象。存储与第一选项数据对象和第二选项数据对象相关联的版本号。存储第一个选项数据对象和项目数据对象之间的链接。存储第二选项数据对象与项目数据对象之间的链接。

6. 发明授权

US07742888B2 Allocating processing units to generate simulated diffraction signals used in optical metrology 失效
$Allocating processing units to generate simulated diffraction signals used in optical metrology$
标题翻译：分配处理单元以产生光学计量学中使用的模拟衍射信号
公开(公告)号：US07742888B2
公开(公告)日：2010-06-22
申请号：US11493290
申请日：2006-07-25
申请人： Hemalatha Erva , Hong Qiu , Junwei Bao , Vi Vuong
发明人： Hemalatha Erva , Hong Qiu , Junwei Bao , Vi Vuong
IPC分类号： G01N37/00
CPC分类号： G01B11/24 , G01N21/4788
摘要： In allocating processing units of a computer system to generate simulated diffraction signals used in optical metrology, a request for a job to generate simulated diffraction signals using multiple processing units is obtained. A number of processing units requested for the job to generate simulated diffraction signals is then determined. A number of available processing units is determined. When the number of processing units requested is greater than the number of available processing units, a number of processing units is assigned to generate the simulated diffraction signals that is less than the number of processing units requested.
摘要翻译：在分配计算机系统的处理单元以产生光学测量中使用的模拟衍射信号时，获得使用多个处理单元产生模拟衍射信号的工作请求。然后确定要求工作产生模拟衍射信号的多个处理单元。确定了许多可用的处理单元。当所请求的处理单元的数量大于可用处理单元的数量时，分配多个处理单元以产生小于所请求的处理单元的数量的模拟衍射信号。

7. 发明授权

US07526354B2 Managing and using metrology data for process and equipment control 失效
标题翻译：管理和使用测量数据进行过程和设备控制
公开(公告)号：US07526354B2
公开(公告)日：2009-04-28
申请号：US11484484
申请日：2006-07-10
申请人： Manuel Madriaga , Junwei Bao , Vi Vuong
发明人： Manuel Madriaga , Junwei Bao , Vi Vuong
IPC分类号： G06F19/00
CPC分类号： G01B11/14 , G01N21/4788 , G01N21/95607 , G01N2021/95615 , G03F7/70625
摘要： A system for examining a patterned structure formed on a semiconductor wafer using an optical metrology model includes a first fabrication cluster, a metrology cluster, an optical metrology model optimizer, and a real time profile estimator. The first fabrication cluster configured to process a wafer, the wafer having a first patterned and a first unpatterned structure. The first patterned structure has underlying film thicknesses, critical dimension, and profile. The metrology cluster including one or more optical metrology devices coupled to the first fabrication cluster. The metrology cluster is configured to measure diffraction signals off the first patterned and the first unpatterned structure. The metrology model optimizer is configured to optimize an optical metrology model of the first patterned structure using one or more measured diffraction signals off the first patterned structure and with floating profile parameters, material refraction parameters, and metrology device parameters.
摘要翻译：使用光学测量模型检查在半导体晶片上形成的图案化结构的系统包括第一制造集群，度量集群，光学计量模型优化器和实时分布估计器。第一制造集群被配置为处理晶片，晶片具有第一图案化和第一未图案化结构。第一图案结构具有底层膜厚度，临界尺寸和轮廓。测量集群包括耦合到第一制造集群的一个或多个光学测量装置。测量簇被配置为测量离开第一图案和第一未图案化结构的衍射信号。计量模型优化器被配置为使用离开第一图案化结构的一个或多个测量的衍射信号以及浮动轮廓参数，材料折射参数和度量设备参数来优化第一图案化结构的光学测量模型。

8. 发明申请

US20080285054A1 OPTICAL METROLOGY OPTIMIZATION FOR REPETITIVE STRUCTURES 失效
标题翻译：重复结构的光学计量优化
公开(公告)号：US20080285054A1
公开(公告)日：2008-11-20
申请号：US12140627
申请日：2008-06-17
申请人： Vi VUONG , Junwei BAO , Joerg BISCHOFF
发明人： Vi VUONG , Junwei BAO , Joerg BISCHOFF
IPC分类号： G01B11/30
CPC分类号： G01N21/4788 , G01B11/24 , G03F7/70625
摘要： An optical metrology model for a structure to be formed on a wafer is developed by characterizing a top-view profile and a cross-sectional view profile of the structure using profile parameters. The profile parameters of the top-view profile and the cross-sectional view profile are integrated together into the optical metrology model. The profile parameters of the optical metrology model are saved.
摘要翻译：通过使用轮廓参数表征结构的顶视图轮廓和横截面视图轮廓来开发用于在晶片上形成的结构的光学测量模型。顶视图轮廓和横截面视图轮廓的轮廓参数集成在光学计量模型中。保存光学计量模型的轮廓参数。

9. 发明授权

US07453584B2 Examining a structure formed on a semiconductor wafer using machine learning systems 失效
标题翻译：使用机器学习系统检查在半导体晶片上形成的结构
公开(公告)号：US07453584B2
公开(公告)日：2008-11-18
申请号：US11869591
申请日：2007-10-09
申请人： Shifang Li , Junwei Bao
发明人： Shifang Li , Junwei Bao
IPC分类号： G01B11/14 , G01B11/24 , G01B7/00
CPC分类号： G03F7/70625
摘要： A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured from the structure using an optical metrology device. A first profile is obtained from a first machine learning system using the first diffraction signal obtained as an input to the first machine learning system. The first machine learning system is configured to generate a profile as an output for a diffraction signal received as an input. A second profile is obtained from a second machine learning system using the first profile obtained from the first machine learning system as an input to the second machine learning system. The second machine learning system is configured to generate a diffraction signal as an output for a profile received as an input. The first and second profiles include one or more parameters that characterize one or more features of the structure.
摘要翻译：通过使用光学测量装置获得从该结构测量的第一衍射信号来检查形成在半导体晶片上的结构。使用作为第一机器学习系统的输入获得的第一衍射信号从第一机器学习系统获得第一轮廓。第一机器学习系统被配置为生成作为作为输入接收的衍射信号的输出的轮廓。使用从第一机器学习系统获得的第一轮廓作为第二机器学习系统的输入，从第二机器学习系统获得第二轮廓。第二机器学习系统被配置为产生衍射信号作为作为输入接收的轮廓的输出。第一和第二简档包括表征结构的一个或多个特征的一个或多个参数。

10. 发明申请

US20080195342A1 OPTICAL METROLOGY MODEL OPTIMIZATION FOR REPETITIVE STRUCTURES 审中-公开
标题翻译：用于重复结构的光学计量模型优化
公开(公告)号：US20080195342A1
公开(公告)日：2008-08-14
申请号：US12099735
申请日：2008-04-08
申请人： Shifang LI , Junwei Bao , Hong Qui , Victor Liu
发明人： Shifang LI , Junwei Bao , Hong Qui , Victor Liu
IPC分类号： G06F19/00
CPC分类号： G03F7/70625 , G01B11/24 , G03F7/705
摘要： An optical metrology model for a repetitive structure is optimized by selecting one or more profile parameters using one or more selection criteria. One or more termination criteria are set, the one or more termination criteria comprising measures of stability of the optical metrology model. The profile shape features of the repetitive structure are characterized using the one or more selected profile parameters. The optical metrology model is optimized using a set of values for the one or more selected profile parameters. One or more profile parameters of the profile of the repetitive structure are determined using the optimized optical metrology model and one or more measured diffraction signals. Values of the one or more termination criteria are calculated using the one or more determined profile parameters. When the calculated values of the one or more termination criteria do not match the one or more set termination criteria, the selection of the one or more profile parameters and/or the characterization of the profile shape features of the repetitive structure are revised.
摘要翻译：通过使用一个或多个选择标准选择一个或多个轮廓参数来优化用于重复结构的光学计量学模型。设置一个或多个终止标准，所述一个或多个终止标准包括光学测量模型的稳定性的测量。使用一个或多个所选择的轮廓参数来表征重复结构的轮廓形状特征。光学测量模型使用一组或多个所选配置文件参数的值进行优化。使用优化的光学测量模型和一个或多个测量的衍射信号来确定重复结构的轮廓的一个或多个轮廓参数。使用一个或多个确定的简档参数来计算一个或多个终止标准的值。当一个或多个终止标准的计算值与一个或多个设定的终止标准不匹配时，修改重复结构的一个或多个简档参数的选择和/或轮廓形状特征的表征。

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