The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. YOLOv7 is a state-of-the-art object detector based on the YOLO family of models which have become popular for industrial applications. One such possible application domain can be semiconductor defect inspection. The performance of any machine learning model depends on its hyperparameters. Furthermore, combining predictions of one or more models in different ways can also affect performance. In this research, we experiment with YOLOv7, a recently proposed, state-of-the-art object detector, by training and evaluating models with different hyperparameters to investigate which ones improve performance in terms of detection precision for semiconductor line space pattern defects. The base YOLOv7 model with default hyperparameters and Non Maximum Suppression (NMS) prediction combining outperforms all RetinaNet models from previous work in terms of mean Average Precision (mAP). We find that vertically flipping images randomly during training yields a 3% improvement in the mean AP of all defect classes. Other hyperparameter values improved AP only for certain classes compared to the default model. Combining models that achieve the best AP for different defect classes was found to be an effective ensembling strategy. Combining predictions from ensembles using Weighted Box Fusion (WBF) prediction gave the best performance. The best ensemble with WBF improved on the mAP of the default model by 10%.

翻译：深度学习目标检测领域不断发展，众多新技术与模型被相继提出。YOLOv7是YOLO系列模型中的先进目标检测器，已广泛应用于工业场景，其中半导体缺陷检测即为典型应用。任何机器学习模型的性能均依赖于其超参数设置，此外，不同模型预测结果的组合方式也会影响最终性能。本研究针对近期提出的先进目标检测器YOLOv7，通过训练并评估不同超参数配置的模型，探究其对半导体线宽图形缺陷检测精度的提升效果。采用默认超参数与非极大值抑制（NMS）预测融合的原始YOLOv7模型，在所有缺陷类别上的平均精度均值（mAP）均优于先前工作中的RetinaNet模型。实验发现，训练时随机垂直翻转图像可使所有缺陷类别的平均精度提升3%；其他超参数调整则仅对特定缺陷类别的平均精度产生改进。通过为不同缺陷类别分别选取最优平均精度的模型进行组合，可形成有效的集成策略。采用加权框融合（WBF）预测进行集成模型的结果融合后，性能达到最优。相较于默认模型，最佳WBF集成模型的mAP提升了10%。