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51. 发明申请

US20100040795A1 PROCESS FOR SEALING MICRO PORES OF MICRO-ARC OXIDE FILMS 审中-公开
标题翻译：密封微弧氧化膜微孔的方法
公开(公告)号：US20100040795A1
公开(公告)日：2010-02-18
申请号：US12478022
申请日：2009-06-04
申请人： FENG-YUEN DAI , CHWAN-HWA CHIANG , YUNG-TA LO , CHI-CHUANG HO , WEI LIU , XU-FENG AO
发明人： FENG-YUEN DAI , CHWAN-HWA CHIANG , YUNG-TA LO , CHI-CHUANG HO , WEI LIU , XU-FENG AO
IPC分类号： B05D1/18
CPC分类号： C25D11/246 , C25D11/26 , C25D11/30
摘要： A process for sealing micro pores of micro-arc oxide film is disclosed in the present disclosure. The process may comprise the following steps: providing a metal coated with a micro-arc oxide film; providing a dipping solution including polyester methacrylate monomer for use as a sealing agent; and dipping the metal in the sealing agent to form a coating on the oxide film's surface.
摘要翻译：在本公开中公开了一种密封微弧氧化膜微孔的方法。该方法可以包括以下步骤：提供涂覆有微弧氧化膜的金属; 提供包含用作密封剂的聚酯甲基丙烯酸酯单体的浸渍溶液; 并将金属浸入密封剂中以在氧化膜的表面上形成涂层。

52. 发明申请

US20100040786A1 PROCESS FOR SEALING MICRO PORES OF MICRO-ARC OXIDE FILMS 审中-公开
标题翻译：密封微弧氧化膜微孔的方法
公开(公告)号：US20100040786A1
公开(公告)日：2010-02-18
申请号：US12478020
申请日：2009-06-04
申请人： FENG-YUEN DAI , CHWAN-HWA CHIANG , YUNG-TA LO , CHI-CHUANG HO , WEI LIU , XU-FENG AO
发明人： FENG-YUEN DAI , CHWAN-HWA CHIANG , YUNG-TA LO , CHI-CHUANG HO , WEI LIU , XU-FENG AO
IPC分类号： B05D3/12 , B05D1/18 , B05D1/02
CPC分类号： C25D11/246 , C25D11/026 , C25D11/26 , C25D11/30
摘要： A process for sealing micro pores of micro-arc oxide film is disclosed in the present disclosure. The process may comprise the following steps: providing a piece of metal having a micro-arc oxide film; preparing a solution including ethyl silicate to make a sealing agent; dipping the metal in the sealing agent to form a coating on the film's surface.
摘要翻译：在本公开中公开了一种密封微弧氧化膜微孔的方法。该方法可以包括以下步骤：提供具有微弧氧化膜的金属片; 制备包括硅酸乙酯制成密封剂的溶液; 将金属浸入密封剂中以在膜的表面上形成涂层。

53. 发明申请

US20090300932A1 WORKPIECE INSPECTING DEVICE 失效
标题翻译：工作检查设备
公开(公告)号：US20090300932A1
公开(公告)日：2009-12-10
申请号：US12324856
申请日：2008-11-27
申请人： WEI LIU
发明人： WEI LIU
IPC分类号： G01B5/02
CPC分类号： G01B3/14 , G01B3/30 , G01B3/32 , G01B5/02 , G01B5/06 , Y10S33/01
摘要： 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.
摘要翻译：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。

54. 发明申请

US20090063077A1 AUTOMATED PROCESS CONTROL USING PARAMETERS DETERMINED WITH APPROXIMATION AND FINE DIFFRACTION MODELS 失效
标题翻译：自动化过程控制使用参数确定与近似和微分散模型
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。

55. 发明申请

US20090063075A1 DETERMINING PROFILE PARAMETERS OF A STRUCTURE USING APPROXIMATION AND FINE DIFFRACTION MODELS IN OPTICAL METROLOGY 失效
标题翻译：使用光学计量学中的近似和微分衍射模型确定结构的剖面参数
公开(公告)号：US20090063075A1
公开(公告)日：2009-03-05
申请号：US11846462
申请日：2007-08-28
申请人： WEI LIU , SHIFANG LI , WEIDONG YANG
发明人： WEI LIU , SHIFANG LI , WEIDONG YANG
IPC分类号： G06F19/00
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。

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