Manufacturing wafers is an intricate task involving thousands of steps. Defect Pattern Recognition (DPR) of wafer maps is crucial to find the root cause of the issue and further improving the yield in the wafer foundry. Mixed-type DPR is much more complicated compared to single-type DPR due to varied spatial features, the uncertainty of defects, and the number of defects present. To accurately predict the number of defects as well as the types of defects, we propose a novel compact deformable convolutional transformer (DC Transformer). Specifically, DC Transformer focuses on the global features present in the wafer map by virtue of learnable deformable kernels and multi-head attention to the global features. The proposed method succinctly models the internal relationship between the wafer maps and the defects. DC Transformer is evaluated on a real dataset containing 38 defect patterns. Experimental results show that DC Transformer performs exceptionally well in recognizing both single and mixed-type defects. The proposed method outperforms the current state of the models by a considerable margin

翻译：晶圆制造是一个涉及数千道工序的复杂过程。晶圆图的缺陷模式识别（DPR）对于查找问题的根本原因以及进一步提高晶圆厂的良率至关重要。与单一类型DPR相比，混合型DPR由于空间特征多样、缺陷不确定性以及缺陷数量差异而更为复杂。为了准确预测缺陷数量及缺陷类型，我们提出了一种新颖的紧凑型可变形卷积Transformer（DC Transformer）。具体而言，DC Transformer通过可学习可变形核及多头注意力机制聚焦晶圆图中的全局特征。所提方法简洁地建模了晶圆图与缺陷之间的内在关系。DC Transformer在包含38种缺陷模式的实际数据集上进行了评估。实验结果表明，DC Transformer在识别单一类型和混合型缺陷方面均表现出色。所提方法以显著优势超越了现有最优模型。