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    • 2. 发明授权
    • Single tool defect classification solution
    • 单刀缺陷分类解决方案
    • US06952653B2
    • 2005-10-04
    • US10705059
    • 2003-11-10
    • Gabor D. TothDavid R. Bakker
    • Gabor D. TothDavid R. Bakker
    • G01N21/95G06T7/00G01B5/28G06K9/00
    • H01L22/20G01N21/9501G06T7/0004G06T2207/30148H01L22/12H01L2924/014
    • Methods and apparatus for efficiently analyzing defects in-line on a wafer by wafer basis are provided. In general terms, embodiments of the present invention provide a simple interface for setting up the entire inspection and defect analysis process in a single set up procedure. The apparatus includes an inspection station for inspecting a specimen for potential defects and a review station for analyzing a sample of the potential defects to determine a classification of such potential defects. The apparatus further includes a computer system having an application interface operable to allow a user to set up the inspection station and the review station during a same setup phase so as to allow the inspection station and the review station to then operate automatically to provide defect information for one or more specimens based on the user set up.
    • 提供了通过晶片来有效地分析晶片上的在线缺陷的方法和装置。 一般来说,本发明的实施例提供了用于在单个设置过程中建立整个检查和缺陷分析过程的简单界面。 该装置包括用于检查潜在缺陷的样本的检查站和用于分析潜在缺陷的样本的检查站,以确定这种潜在缺陷的分类。 该装置还包括具有应用接口的计算机系统,其可操作以允许用户在相同的建立阶段期间建立检查站和审查站,以便允许检查站和审查站自动地进行操作以提供缺陷信息 基于用户设置的一个或多个标本。
    • 3. 发明授权
    • Apparatus and methods for searching through and analyzing defect images and wafer maps
    • 用于搜索和分析缺陷图像和晶片图的装置和方法
    • US07283659B1
    • 2007-10-16
    • US10175229
    • 2002-06-18
    • David R. BakkerPrashant A. AjiJames L. BelliveauChacko C. Neroth
    • David R. BakkerPrashant A. AjiJames L. BelliveauChacko C. Neroth
    • G06K9/00
    • G06K9/6254G06K9/6272G06T7/0004G06T2207/30148
    • Disclosed are methods and apparatus for automatically organizing and/or analyzing a plurality of defect images without first providing a predefined set of classified images (herein referred to as a training set). In other words, sorting is not based on a training set or predefined classification codes for such defect images. In one embodiment, the defect images each include associated identifying data, such as a fabrication identifier, lot number, wafer number, and layer identifier. Initially, the defect images are sorted according to at least a portion of the associated identifying data into a plurality of “identifying data groups” or image families. The defect data in each identifying data group is then automatically sorted according to defect appearance. That is, similar defect images are associated with a single bin and similar bins are associated with other similar bins. For example, similar bins are arranged next to each other within a graphical user interface (GUI). A representative feature vector (herein referred to as a “centroid”) is then associated with each bin. The centroid generally represents the images within the particular bin. A search for images that “look like” a specified target image may then be efficiently performed on a particular identifying data group using the centroids of each bin. The target image's feature vector is compared with the centroid of each bin that is within the same identifying data group or image family as the target image. The techniques of the present invention may also be applied to wafer maps, as well as defect images.
    • 公开了用于自动组织和/或分析多个缺陷图像的方法和装置,而不首先提供预定义的分类图像集(这里称为训练集)。 换句话说,排序不是基于这样的缺陷图像的训练集或预定义的分类代码。 在一个实施例中,缺陷图像各自包括关联的识别数据,例如制造标识符,批号,晶片号和层标识符。 最初,根据关联的识别数据的至少一部分将缺陷图像分类成多个“识别数据组”或图像族。 然后根据缺陷外观自动对每个识别数据组中的缺陷数据进行分类。 也就是说,类似的缺陷图像与单个仓相关联,并且类似的仓与其他相似的仓相关联。 例如,类似的箱体在图形用户界面(GUI)内彼此相邻布置。 然后将代表特征向量(这里称为“质心”)与每个仓相关联。 质心通常表示特定仓内的图像。 然后可以使用每个仓的质心在特定的识别数据组上有效地执行“看起来像”指定的目标图像的图像的搜索。 将目标图像的特征向量与与目标图像相同的识别数据组或图像系列内的每个仓的质心进行比较。 本发明的技术也可以应用于晶片图以及缺陷图像。