In this research, we introduce a unified end-to-end Automated Defect Classification-Detection-Segmentation (ADCDS) framework for classifying, detecting, and segmenting multiple instances of semiconductor defects for advanced nodes. This framework consists of two modules: (a) a defect detection module, followed by (b) a defect segmentation module. The defect detection module employs Deformable DETR to aid in the classification and detection of nano-scale defects, while the segmentation module utilizes BoxSnake. BoxSnake facilitates box-supervised instance segmentation of nano-scale defects, supported by the former module. This simplifies the process by eliminating the laborious requirement for ground-truth pixel-wise mask annotation by human experts, which is typically associated with training conventional segmentation models. We have evaluated the performance of our ADCDS framework using two distinct process datasets from real wafers, as ADI and AEI, specifically focusing on Line-space patterns. We have demonstrated the applicability and significance of our proposed methodology, particularly in the nano-scale segmentation and generation of binary defect masks, using the challenging ADI SEM dataset where ground-truth pixelwise segmentation annotations were unavailable. Furthermore, we have presented a comparative analysis of our proposed framework against previous approaches to demonstrate its effectiveness. Our proposed framework achieved an overall mAP@IoU0.5 of 72.19 for detection and 78.86 for segmentation on the ADI dataset. Similarly, for the AEI dataset, these metrics were 90.38 for detection and 95.48 for segmentation. Thus, our proposed framework effectively fulfils the requirements of advanced defect analysis while addressing significant constraints.

翻译：本研究提出了一种统一的端到端自动缺陷分类-检测-分割（ADCDS）框架，用于对先进工艺节点的半导体缺陷进行多实例分类、检测与分割。该框架包含两个模块：（a）缺陷检测模块，以及（b）缺陷分割模块。缺陷检测模块采用Deformable DETR辅助实现纳米级缺陷的分类与检测，而分割模块则利用BoxSnake。BoxSnake在前一模块的支持下，实现了纳米级缺陷的框监督实例分割。这简化了流程，消除了传统分割模型训练通常需要人工专家进行费时费力的真实像素级掩码标注的要求。我们使用来自实际晶圆的两个不同工艺数据集（即ADI和AEI）评估了ADCDS框架的性能，特别聚焦于线-空图形。我们证明了所提方法的适用性与重要性，尤其是在具有挑战性的ADI扫描电镜数据集上实现了纳米级分割及二值缺陷掩码的生成，而该数据集原本缺乏真实的像素级分割标注。此外，我们通过对比分析展示了所提框架相较于以往方法的有效性。在ADI数据集上，我们提出的框架在检测任务上实现了72.19的整体mAP@IoU0.5，在分割任务上实现了78.86。同样，在AEI数据集上，这些指标分别为检测90.38和分割95.48。因此，所提框架在满足先进缺陷分析需求的同时，有效应对了重要的约束条件。