Mixed-type Wafer Defect Datasets

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.

晶圆图缺陷图案识别（Defect Pattern Recognition, DPR）是确定生产缺陷根本成因的关键环节，可为晶圆代工厂的良率改善提供参考方向。在晶圆制造过程中，单张晶圆上可能同时耦合多种类型的缺陷，此类场景被称为混合型缺陷图案识别（Mixed-type Defects DPR）。混合型缺陷的检测难度显著更高，因为缺陷的组合方式存在极大差异，涵盖缺陷类型、位置、角度、缺陷数量等多个维度。深度学习方法已成为解决复杂图案识别问题的优选方案。本文针对单张晶圆上耦合多种缺陷的混合型缺陷图案识别（Mixed-type DPR, MDPR）任务，提出了一种可变形卷积网络（Deformable Convolutional Network, DC-Net）。该网络设计了可变形卷积单元，可对混合型缺陷进行选择性采样，进而从晶圆图中提取高质量特征。研究采用独热编码（One-hot Encoding）机制对多标签输出层进行改进，将提取到的混合型特征分解为各个基础单缺陷特征。实验结果表明，所提出的DC-Net模型性能优于传统模型及其他深度学习模型。可解释性分析的进一步结果显示，所提出的DC-Net可精准定位含噪声点的晶圆图缺陷区域，这对混合型缺陷图案识别任务具有重要应用价值。

搜集汇总

背景与挑战

背景概述

Mixed-type Wafer Defect Datasets是一个用于晶圆缺陷模式识别的数据集，特别关注混合型缺陷的识别。数据集包含52x52的图像数据，标记了正常和损坏的芯片，并使用one-hot编码表示8种基本缺陷类型和29种混合型缺陷类型。