After wafer fabrication, individual chips on the wafer are checked for defects by using multiple electrical tests. The test results can be represented by binary values for all individual chips, which form a spatial map called a wafer bin map (WBM). Different defect patterns in WBMs are related to different causes of process faults. Thus, it is important to classify WBMs according to their defect patterns to identify the root causes of process faults and correct the problems. Recently, with the increase in wafer size, the semiconductor manufacturing process has become more complicated and the probability of having mixed-type defect patterns in WBMs has increased. Previous studies for the classification of mixed-type defect patterns have mainly used labeled WBM data, although a much larger quantity of unlabeled data are often available in practice. To utilize both labeled and unlabeled data to achieve better classification performance, this study proposes the use of a semi-supervised deep convolutional generative model. In particular, we formulate the problem of classifying mixed-type defect patterns as a problem of multi-label classification and adopt multiple latent class variables, each for a distinct single pattern. As an inherent advantage of a generative model, we can also use the proposed model to generate new WBM data.

中文翻译：

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半监督多标签学习，用于对具有混合类型缺陷模式的晶圆仓图进行分类

晶圆制造完成后，晶圆上的单个芯片会通过多次电气测试来检查缺陷。测试结果可以用所有单个芯片的二进制值表示，形成一个称为晶圆仓图 (WBM) 的空间图。WBM 中的不同缺陷模式与过程故障的不同原因有关。因此，根据缺陷模式对 WBM 进行分类以识别过程故障的根本原因并纠正问题非常重要。最近，随着晶圆尺寸的增加，半导体制造工艺变得更加复杂，并且 WBM 中出现混合型缺陷图案的可能性也增加了。以前对混合型缺陷模式分类的研究主要使用标记的 WBM 数据，尽管在实践中通常可以使用大量未标记的数据。为了利用标记和未标记的数据来实现更好的分类性能，本研究建议使用半监督深度卷积生成模型。特别是，我们将混合类型缺陷模式的分类问题表述为多标签分类问题，并采用多个潜在类变量，每个变量对应一个不同的单一模式。作为生成模型的固有优势，我们还可以使用所提出的模型来生成新的 WBM 数据。每个都有一个独特的单一模式。作为生成模型的固有优势，我们还可以使用所提出的模型来生成新的 WBM 数据。每个都有一个独特的单一模式。作为生成模型的固有优势，我们还可以使用所提出的模型来生成新的 WBM 数据。