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：随着半导体领域图案尺寸的持续缩小，由于随机噪声的存在以及缺陷图案和类型的动态行为等因素，缺陷检测变得越来越困难。传统基于规则的方法和KNN等非参数监督机器学习算法在先进节点的半导体缺陷检测需求中大多失效。基于深度学习的方法在半导体缺陷检测领域逐渐流行，因为其在应对这些挑战性场景时表现出鲁棒性。在本研究中，我们提出了一种基于深度学习的自动化方法，用于在扫描电子显微镜图像中高效定位和分类缺陷。我们提出了SEMI-CenterNet，一种定制的CenterNet架构，该架构使用半导体晶圆缺陷的SEM图像进行训练。与以往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-CenterNet经过训练后能够输出缺陷实例的中心、类别、尺寸和偏移量。这与大多数使用锚点进行边界框预测的目标检测模型的方法不同。以往的方法预测冗余边界框，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-CenterNet，并针对该框架对两个ResNet骨干网络进行基准测试。初始阶段，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。主要结果表明，SEMI-CenterNet在推理时间上相较于以往研究工作有显著提升。最后，从ADI数据集到AEI数据集（反之亦然）应用迁移学习（使用自定义SEM数据集的权重），相较于传统训练方法，该方法减少了两个骨干网络达到最佳平均精度所需的训练时间。