A wafer consists of several chips, and serial electrical tests are conducted for each chip to investigate whether the chip is defective. A bin indicates the test results for each chip with information on which tests the chip failed. A wafer bin map (WBM) shows the locations and bins of the defects on the wafer. WBMs showing spatial patterns of defects usually result from assignable causes in the wafer fabrication process; hence, they should be classified in advance. The existing defect-pattern taxonomies do not consider bins, although useful information can be obtained from them. We propose a taxonomy that consists of the shape, size, location, and bin dimensions. The bin dimension is developed using Bin2Vec method, which determines RGB (red-green-blue) code for each bin according to the spatial similarity between bins. Three levels of the bin dimension are defined by analyzing a large number of WBMs using Bin2Vec and clustering methods. Compared with the existing taxonomies, the proposed taxonomy has the advantage of identifying major bins of defect patterns, new defect patterns, and non-critical defect patterns. A high-quality training dataset was obtained using the proposed taxonomy; consequently, a defect pattern classification model with satisfactory classification performance could be obtained.

一块晶圆由多个芯片组成，对每个芯片进行串行电性测试，以检查该芯片是否有缺陷。箱指示每个芯片的测试结果以及芯片失败的测试的信息。晶圆仓图 (WBM) 显示晶圆上缺陷的位置和仓。显示缺陷空间模式的 WBM 通常是由晶圆制造过程中的可指定原因造成的；因此，应提前对它们进行分类。现有的缺陷模式分类法不考虑容器，尽管可以从中获得有用的信息。我们提出了一种由形状、大小、位置和箱尺寸组成的分类法。bin维度是使用Bin2Vec方法开发的，该方法根据bin之间的空间相似性确定每个bin的RGB（红-绿-蓝）编码。通过使用 Bin2Vec 和聚类方法分析大量 WBM，定义了三个级别的 bin 维度。与现有的分类法相比，所提出的分类法具有识别主要缺陷模式、新缺陷模式和非关键缺陷模式的优点。使用所提出的分类法获得了高质量的训练数据集；因此，可以获得具有令人满意的分类性能的缺陷图案分类模型。