Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like KNN mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method.

翻译：半导体领域中图案尺寸的持续缩小，加上随机噪声的存在以及缺陷图案和类型的动态行为，使得缺陷检测日益困难。传统的基于规则的方法和非参数监督机器学习算法（如KNN）大多无法满足先进节点下半导体缺陷检测的需求。基于深度学习的方法因被证明对这些挑战性场景具有鲁棒性，在半导体缺陷检测领域广受欢迎。本研究提出了一种基于深度学习的自动化方法，用于高效定位和分类扫描电子显微镜（SEM）图像中的缺陷。我们提出了SEMI-CenterNet（SEMI-CN），这是一种定制的CenterNet架构，基于半导体晶圆缺陷的SEM图像进行训练。与先前研究的深度学习模型相比，所提出的CenterNet方法提高了计算效率。SEMI-CN经过训练，可输出缺陷实例的中心、类别、尺寸和偏移量。这与大多数使用锚框进行边界框预测的目标检测模型方法不同。先前方法会预测冗余边界框，其中大部分在后处理中被丢弃。CenterNet通过仅预测可能缺陷中心点的边界框来缓解这一问题。我们在两个数据集上训练SEMI-CN，并针对该框架基准测试了两个ResNet骨干网络。首先，在COCO数据集上预训练的ResNet模型分别使用两个数据集进行训练。最终，SEMI-CN在推理时间上相较于先前研究显示出显著改善。此外，将迁移学习（使用自定义SEM数据集的权重）从ADI数据集应用于AEI数据集及其反向应用，相较于传统训练方法，在达到最佳平均精度（mAP）时，两个骨干网络所需的训练时间均有所减少。