Lightweight Hybrid Wafer Defect Pattern Network Based on Feedforward Efficient Attention

ABSTRACT With the increase of semiconductor integration density, in order to cope with the increase of wafer defect complexity and types, especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects, this study proposes a lightweight model for wafer defect detection called LightWMNet. First, using a hierarchical attention Encoder‐Decoder architecture, the features of wafer defect pattern (WDP) are channel recalibrated to generate high‐resolution fine‐grained features and low‐resolution coarse‐grained features. Secondly, the backbone network incorporates two novel attention modules—feedforward spatial attention (FFSa) and feedforward channel attention (FFCa)—to amplify responses in critical defect regions and suppress noise from stochastic discrete pixels. These mechanisms synergistically enhance feature discriminability without introducing significant parametric overhead. Finally, the Dice loss function and the cross entropy loss function are combined to jointly evaluate the segmentation and classification accuracy of the model. Experimental results on the public mixed wafer defect dataset MixedWM38 show that the pixel accuracy (PA), intersection over union (IoU) and Dice coefficient of the proposed network reach 98.26%, 94.83% and 97.22%, respectively. Without significantly increasing the computational complexity and size of the model, compared with the existing state‐of‐the‐art (SOTA) model, the classification accuracy of lightWMNet in single defect, three mixed defects and four mixed defects is improved by 0.5%, 0.25% and 0.89% respectively. Furthermore, we used transfer learning for the first time to evaluate the model's generalisation ability for unseen defect categories. The results showed that LightWMNet still has a certain recognition ability even in untrained wafer defects.