In semiconductor manufacturing, wafer testing is performed to ensure the performance of each product after wafer fabrication. The wafer map is used to visualize the color‐coded wafer test results based on the locations. The defects on the wafer map may be randomly distributed or form clustered patterns. The various clustered defect patterns are usually caused by assignable faults. The identification of the patterns is thus important to provide valuable hints for the root causes diagnosis. Solving the problems helps improve the manufacturing processes and reduce costs. In this study, we present a novel convolutional neural network (CNN)–based method to automatically recognize the defect pattern on wafer maps. Our method uses polar mapping before the training of CNN to transform the circular wafer map into a matrix, which can be processed within CNN architecture. This procedure also reduces the input size and solves variations in wafer sizes and die sizes. To eliminate the effects of rotation, we apply data augmentation in the training of CNN. Experiments using the real‐world dataset prove the effectiveness and superiority of our method.

中文翻译：

---

使用卷积神经网络识别晶圆上的缺陷图案

在半导体制造中，进行晶片测试以确保晶片制造后每种产品的性能。晶圆图用于根据位置可视化以颜色编码的晶圆测试结果。晶圆图上的缺陷可以随机分布，也可以形成簇状图案。各种群集缺陷模式通常是由可分配的故障引起的。因此，模式识别对于为根本原因诊断提供有价值的提示很重要。解决问题有助于改善制造过程并降低成本。在这项研究中，我们提出了一种基于卷积神经网络（CNN）的新颖方法来自动识别晶圆图上的缺陷图案。我们的方法在训练CNN之前使用极坐标映射将圆形晶圆图转换为矩阵，可以在CNN架构中进行处理。该过程还减小了输入尺寸，并解决了晶圆尺寸和管芯尺寸的变化。为了消除旋转的影响，我们在CNN的训练中应用了数据增强。使用真实数据集进行的实验证明了我们方法的有效性和优越性。