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    • 4. 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。