公开(公告)号：US20090063077A1

公开(公告)日：2009-03-05

申请号：US11848214

申请日：2007-08-30

申请人： WEI LIU ,  SHIFANG LI ,  WEIDUNG YANG ,  MANUEL MADRIAGA

发明人： WEI LIU ,  SHIFANG LI ,  WEIDUNG YANG ,  MANUEL MADRIAGA

IPC分类号： G06F19/00

CPC分类号： G05B19/41875 ,  G01N21/4788 ,  G03F7/70625 ,  G05B2219/32182 ,  G05B2219/32186 ,  G05B2219/32188 ,  G05B2219/37224

摘要： Provided is a method of controlling a fabrication cluster using a machine learning system, the machine learning system trained developed using an optical metrology model, the optical metrology model comprising a profile model, an approximation diffraction model, and a fine diffraction model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signal is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters. A first machine learning system is trained using the pairs of difference diffraction signal and corresponding profile parameters. A library of simulated fine diffraction signals and profile parameters is generated using the trained first machine learning system and using ranges and corresponding resolutions of the profile parameters. The library is used to train a second machine learning system. A measured diffraction signal is input into the trained second machine learning system to determine at least one profile parameter. The at least one profile parameter is used to adjust at least one process parameter or equipment setting of the fabrication cluster.

摘要翻译： 提供了一种使用机器学习系统来控制制造集群的方法，使用光学测量模型训练的机器学习系统，包括轮廓模型，近似衍射模型和精细衍射模型的光学测量模型。 基于结构的近似衍射模型生成模拟近似衍射信号。 通过从每个模拟的细衍射信号中减去模拟近似衍射信号并与相应的轮廓参数配对来获得差分衍射信号。 使用差分衍射信号和相应的轮廓参数对来训练第一机器学习系统。 使用训练有素的第一机器学习系统并使用轮廓参数的范围和相应的分辨率来生成模拟的细衍射信号和轮廓参数的库。 该图书馆用于训练第二台机器学习系统。 测量的衍射信号被输入到训练有素的第二机器学习系统中以确定至少一个轮廓参数。 至少一个轮廓参数用于调整至少一个制造集群的过程参数或设备设置。

公开(公告)号：US07729873B2

公开(公告)日：2010-06-01

申请号：US11846462

申请日：2007-08-28

申请人： Wei Liu ,  Shifang Li ,  Weidung Yang

发明人： Wei Liu ,  Shifang Li ,  Weidung Yang

IPC分类号： G06F19/00 ,  G01N37/00 ,  G01B11/14

CPC分类号： G01B11/24 ,  G03F7/70625

摘要： Provided is a method for determining one or more profile parameters of a structure using an optical metrology model, the optical metrology model comprising a profile model, an approximation diffraction model, and a fine diffraction model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signals is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters and used to generate a library of difference diffraction signals. A measured diffraction signal adjusted by the simulated approximation diffraction signal is matched against the library to determine at least one profile parameter of the structure.

摘要翻译： 提供了一种用于使用光学测量模型来确定结构的一个或多个轮廓参数的方法，光学测量模型包括轮廓模型，近似衍射模型和细小的衍射模型。 基于结构的近似衍射模型生成模拟近似衍射信号。 通过从每个模拟的细衍射信号中减去模拟的近似衍射信号并与相应的轮廓参数配对并用于产生差分衍射信号的文库，获得一组差分衍射信号。 通过模拟近似衍射信号调整的测量的衍射信号与文库匹配以确定结构的至少一个轮廓参数。

公开(公告)号：US07627392B2

公开(公告)日：2009-12-01

申请号：US11848214

申请日：2007-08-30

申请人： Wei Liu ,  Shifang Li ,  Weidung Yang ,  Manuel Madriaga

发明人： Wei Liu ,  Shifang Li ,  Weidung Yang ,  Manuel Madriaga

IPC分类号： G06F19/00

CPC分类号： G05B19/41875 ,  G01N21/4788 ,  G03F7/70625 ,  G05B2219/32182 ,  G05B2219/32186 ,  G05B2219/32188 ,  G05B2219/37224

摘要： Provided is a method of controlling a fabrication cluster using a machine learning system, the machine learning system trained developed using an optical metrology model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signal is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters. A first machine learning system is trained using the pairs of difference diffraction signal and corresponding profile parameters. A library of simulated fine diffraction signals and profile parameters is generated using the trained first machine learning system and using ranges and corresponding resolutions of the profile parameters. A measured diffraction signal is input into the trained second machine learning system to determine at least one profile parameter. The at least one profile parameter is used to adjust at least one process parameter or equipment setting of the fabrication cluster.

摘要翻译： 提供了一种使用机器学习系统来控制制造集群的方法，使用光学计量学模型训练的机器学习系统。 基于结构的近似衍射模型生成模拟近似衍射信号。 通过从每个模拟的细衍射信号中减去模拟近似衍射信号并与相应的轮廓参数配对来获得差分衍射信号。 使用差分衍射信号和相应的轮廓参数对来训练第一机器学习系统。 使用训练有素的第一机器学习系统并使用轮廓参数的范围和相应的分辨率来生成模拟的细衍射信号和轮廓参数的库。 测量的衍射信号被输入到训练有素的第二机器学习系统中以确定至少一个轮廓参数。 至少一个轮廓参数用于调整至少一个制造集群的过程参数或设备设置。