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    • 86. 发明授权
    • Workpiece inspecting device
    • 工件检查装置
    • US07748133B2
    • 2010-07-06
    • US12324856
    • 2008-11-27
    • Wei Liu
    • Wei Liu
    • G01B5/02
    • G01B3/14G01B3/30G01B3/32G01B5/02G01B5/06Y10S33/01
    • An exemplary workpiece inspecting device includes a base, a slider, a handle, a number of magnets, a number of probes and a measuring block. The slider is slidably mounted on the base and supports the handle thereon. The magnets are respectively embedded into the base for attracting and fixing a workpiece on the base. The probes are separately disposed on both the base and the slider for inspecting the perforations defined on a workpiece. The measuring block is attached on the slider for sliding and inspecting a thickness of the workpiece during the movement of the slider.
    • 示例性的工件检查装置包括基座,滑块,手柄,多个磁体,多个探针和测量块。 滑块可滑动地安装在基座上并在其上支撑手柄。 磁体分别嵌入基座中,用于在基座上吸引和固定工件。 探针分别设置在基座和滑块上,用于检查在工件上限定的穿孔。 测量块安装在滑块上,用于在滑块移动期间滑动和检查工件的厚度。
    • 90. 发明授权
    • Automated process control using parameters determined with approximation and fine diffraction models
    • 使用近似和精细衍射模型确定的参数进行自动过程控制
    • US07627392B2
    • 2009-12-01
    • US11848214
    • 2007-08-30
    • Wei LiuShifang LiWeidung YangManuel Madriaga
    • Wei LiuShifang LiWeidung YangManuel Madriaga
    • G06F19/00
    • G05B19/41875G01N21/4788G03F7/70625G05B2219/32182G05B2219/32186G05B2219/32188G05B2219/37224
    • 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.
    • 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。