Abstract. Defects in semiconductor processes can limit yield, increase overall production cost, and also lead to time-dependent critical component failures. Current state-of-the-art optical and electron beam (EB) inspection systems rely on rule-based techniques for defect detection and classification, which are usually rigid in their comparative processes. This rigidity limits overall capability and increases relative engineering time to classify nuisance defects. This is further challenged due to shrinkage of pattern dimensions for advanced nodes. We propose a deep learning-based workflow that circumvents these challenges and enables accurate defect detection, classification, and localization in a unified framework. In particular, we train convolutional neural network-based models using high-resolution EB images of wafers patterned with various types of intentional defects and achieve robust defect detection and classification performance. Furthermore, we generate class activation maps to demonstrate defect localization capability of the model “without” explicitly training it with defect location information. To understand the underlying decision-making process of these deep models, we analyze the learned filters in pixel space and Fourier space and interpret the various operations at different layers. We achieve high sensitivity (97%) and specificity (100%) along with rapid and accurate defect localization. We also test performance of the proposed workflow on images from two distinct patterns and find that in order to retain high accuracy a modest level of retraining is necessary.

中文翻译：

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基于深度学习的半导体工艺缺陷检测、分类和定位

摘要。半导体工艺中的缺陷会限制产量，增加总体生产成本，还会导致与时间相关的关键组件故障。当前最先进的光学和电子束 (EB) 检测系统依赖于基于规则的缺陷检测和分类技术，这些技术在比较过程中通常是刚性的。这种刚性限制了整体能力并增加了对有害缺陷进行分类的相对工程时间。由于高级节点的图案尺寸缩小，这进一步受到挑战。我们提出了一种基于深度学习的工作流程，可以规避这些挑战，并在统一框架中实现准确的缺陷检测、分类和定位。尤其，我们使用带有各种类型故意缺陷图案的晶圆的高分辨率 EB 图像训练基于卷积神经网络的模型，并实现强大的缺陷检测和分类性能。此外，我们生成类激活图来展示模型的缺陷定位能力，“没有”用缺陷位置信息显式训练它。为了理解这些深度模型的底层决策过程，我们分析了像素空间和傅里叶空间中的学习滤波器，并解释了不同层的各种操作。我们实现了高灵敏度 (97%) 和特异性 (100%) 以及快速准确的缺陷定位。