Defect pattern recognition (DPR) of wafer maps is critical for determining the root cause of production defects, which can provide insights for the yield improvement in wafer foundries. During wafer fabrication, several types of defects can be coupled together in a piece of wafer, it is called mixed-type defects DPR. To detect mixed-type defects is much more complicated because the combination of defects may vary a lot, from the type of defects, position, angle, number of defects, etc. Deep learning methods have been a good choice for complex pattern recognition problems. In this article, we propose a deformable convolutional network (DC-Net) for mixed-type DPR (MDPR) in which several types of defects are coupled together in a piece of wafer. A deformable convolutional unit is designed to selectively sample from mixed defects, then extract high-quality features from wafer maps. A multi-label output layer is improved with a one-hot encoding mechanism, which decomposes extract mixed features into each basic single defect. The experiment results indicate that the proposed DC-Net model outperforms conventional models and other deep learning models. Further results of the interpretable analysis reveal that the proposed DC-Net can accurately pinpoint the defects areas of wafer maps with noise points, which is beneficial for mixed-type DPR problems.

中文翻译：

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用于高效混合型晶圆缺陷模式识别的可变形卷积网络

晶圆图的缺陷模式识别 (DPR) 对于确定生产缺陷的根本原因至关重要，这可以为晶圆代工厂的良率提高提供见解。在晶圆制造过程中，在一块晶圆中可以将多种类型的缺陷耦合在一起，称为混合型缺陷 DPR。检测混合型缺陷要复杂得多，因为缺陷的组合可能有很大差异，从缺陷的类型、位置、角度、缺陷数量等来看，深度学习方法一直是复杂模式识别问题的不错选择。在本文中，我们提出了一种用于混合型 DPR (MDPR) 的可变形卷积网络 (DC-Net)，其中几种类型的缺陷在一块晶片中耦合在一起。一个可变形的卷积单元被设计成有选择地从混合缺陷中采样，然后从晶圆图中提取高质量的特征。多标签输出层采用one-hot编码机制进行改进，将提取的混合特征分解为每个基本的单一缺陷。实验结果表明，所提出的 DC-Net 模型优于传统模型和其他深度学习模型。可解释分析的进一步结果表明，所提出的 DC-Net 可以准确地定位带有噪声点的晶圆图的缺陷区域，这有利于混合型 DPR 问题。