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    • 9. 发明授权
    • System and method for classifying, detecting, and counting micropipes
    • 微孔分类,检测和计数的系统和方法
    • US07201799B1
    • 2007-04-10
    • US10997328
    • 2004-11-24
    • Vamsi Velidandla
    • Vamsi Velidandla
    • C30B25/12
    • C30B29/36C30B25/16
    • An automated, non-invasive method for classifying, detecting, and counting micropipes contained within silicon wafers, and generally any assortment of transparent wafers. Classifying, detecting, and counting micropipes takes place through the use of a data processing algorithm that incorporates information regarding: defect size; pit signature; area of pit signature when comparing a topography, specular, or scatter images; and detecting a tail within the standard pit signature. The method of the present invention teaches the development of a topography defect map, specular defect map, and scatter defect map for a complete analysis of the surface of a particular transparent wafer. Conventional detection, classification, and counting of micropipes involve characterization of micropipes in a manual fashion and rely upon an extremely invasive form of sample preparation. The method disclosed in the present invention is completely automated and non-invasive with regards to the treatment of the transparent wafer to be analyzed.
    • 一种用于分类,检测和计数硅晶片内的微孔以及通常各种透明晶片的自动化非侵入性方法。 通过使用包含以下信息的数据处理算法进行分类,检测和计数微孔:缺陷尺寸; 坑签名; 比较地形,镜面或散射图像时的坑签名区域; 并检测标准坑签名内的尾部。 本发明的方法教导了用于完整分析特定透明晶片的表面的形貌缺陷图,镜面缺陷图和散射缺陷图。 微管的常规检测,分类和计数涉及以手动方式表征微管,并且依赖于极其侵入性的样品制备形式。 关于待分析透明晶片的处理,本发明公开的方法是完全自动化且非侵入性的。
    • 10. 发明授权
    • Detecting micropipes
    • 检测微管
    • US07592616B1
    • 2009-09-22
    • US11563996
    • 2006-11-28
    • Vamsi Velidandla
    • Vamsi Velidandla
    • G01N21/88G01N21/86G01N21/00
    • C30B29/36C30B25/16
    • An automated, non-invasive method for classifying, detecting, and counting micropipes contained within silicon wafers, and generally any assortment of transparent wafers. Classifying, detecting, and counting micropipes takes place through the use of a data processing algorithm that incorporates information regarding: defect size; pit signature; area of pit signature when comparing a topography, specular, or scatter images; and detecting a tail within the standard pit signature. The method of the present invention teaches the development of a topography defect map, specular defect map, and scatter defect map for a complete analysis of the surface of a particular transparent wafer. Conventional detection, classification, and counting of micropipes involve characterization of micropipes in a manual fashion and rely upon an extremely invasive form of sample preparation. The method disclosed in the present invention is completely automated and non-invasive with regards to the treatment of the transparent wafer to be analyzed.
    • 一种用于分类,检测和计数硅晶片内的微孔以及通常各种透明晶片的自动化非侵入性方法。 通过使用包含以下信息的数据处理算法进行分类,检测和计数微孔:缺陷尺寸; 坑签名; 比较地形,镜面或散射图像时的坑签名区域; 并检测标准坑签名内的尾部。 本发明的方法教导了用于完整分析特定透明晶片的表面的形貌缺陷图,镜面缺陷图和散射缺陷图。 微管的常规检测,分类和计数涉及以手动方式表征微管,并且依赖于极其侵入性的样品制备形式。 关于待分析透明晶片的处理,本发明公开的方法是完全自动化且非侵入性的。