Abstract: Aiming at the challenges in current wafer defect detection algorithms, which struggled to balance detection accuracy, the number of model parameters, and computational volume, a YOLOv8-based lightweight defect detection on wafers (YOLOv8_LDW) is proposed. First, by fusing the inverted residual mechanism and the dilation reparameterization module, the C2f_IDR module is designed and introduced into the backbone network, which enhances the model’s ability to jointly model the global context information and local detail features of complex defects, while improving reasoning efficiency. Secondly, the high-level screening path aggregation network (HSPAN) is proposed for the first time. The neck network is reconstructed through a bidirectional screening and fusion mechanism, which achieves efficient aggregation of multi-scale features and effectively suppresses the interference of redundant features. Finally, in order to further improve the model’s attention to tiny defects and the regression accuracy of complex shape defects, the Focaler-Shape IoU loss function is used to replace the traditional CIoU loss function. Experimental results show that the F1 Score and mAP50 of the improved model on the real wafer defect dataset reach 97.2% and 98.3%, respectively, which are improvements of 1.4% and 0.8% compared with the baseline model. The number of parameters and computational volume are reduced by 42.5% and 22.2%, respectively, and the model size is only 3.69 MB. In addition, the improved model is validated on the public wafer defect dataset, where Recall, F1 Score, and mAP50 are improved by 7.2%, 1.8% and 2.0%, respectively, compared to the original model. These results demonstrate strong generalization ability and robustness, effectively adapting to the data distribution of different defect types. This demonstrates that the improved algorithm significantly reduces the number of model parameters and computational costs while maintaining high detection accuracy, meeting the practical application requirements for high efficiency and lightweight design in wafer defect detection.

Key words: wafer defect detection, YOLOv8n, lightweight, Focaler-Shape IoU, high-level screening path aggregation network (HSPAN)

摘要： 针对当前晶圆缺陷检测算法在检测精度、模型参数量和计算量之间难以兼顾的问题，提出一种基于YOLOv8的轻量化晶圆缺陷检测算法（YOLOv8-based lightweight defect detection on wafers，YOLOv8_LDW）。通过融合倒置残差机制和膨胀重参数化模块，设计了C2f_IDR模块并引入主干网络中，增强了模型对复杂缺陷全局上下文信息与局部细节特征的联合建模能力，同时提升推理效率。提出高级筛选路径聚合网络（high-level screening path aggregation network，HSPAN），通过双向筛选与融合机制对颈部网络进行重构，实现了多尺度特征的高效聚合，并有效抑制了冗余特征的干扰。为了进一步提升模型对微小缺陷的关注度以及复杂形状缺陷的回归精度，采用Focaler-Shape IoU损失函数替换传统CIoU损失函数。实验结果表明，改进模型在真实晶圆缺陷数据集上的F1 Score和mAP50分别达到97.2%和98.3%，较基线模型提升1.4%和0.8%，参数量和计算量分别降低了42.5%和22.2%，模型大小仅为3.69?MB。此外，改进模型在公共数据集Wafer Defect上进行验证，相较于原模型，R、F1 Score和mAP50分别提升了7.2%、1.8%和2.0%，展现了较强的泛化能力和鲁棒性，可有效适应不同缺陷类型的数据分布。由此表明，改进后的算法能够在保持高检测精度的同时，大幅降低模型参数量和计算成本，满足晶圆缺陷检测对高效性和轻量化的实际应用需求。

关键词: 晶圆缺陷检测, YOLOv8n, 轻量化, Focaler-Shape IoU, 高级筛选路径聚合网络（HSPAN）