ADF-SN: An Adaptive Dual-Domain Fusion and Self-Supervised Network for Wafer Image Denoising

Wafer image denoising is essential in semiconductor manufacturing, impacting yield and defect detection accuracy. Unlike general images, wafer images exhibit unique micrometer-scale structures, low-contrast defects, and fine edges, making denoising particularly challenging. However, traditional methods perform poorly with complex noise, and supervised learning methods rely on clean labeled data that is difficult to acquire, limiting robustness in practical applications. In this study, we propose an adaptive dual-domain fusion and self-supervised network (ADF-SN). First, noise suppression and edge enhancement are performed in the transform and spatial domains to obtain complementary feature representations. Then, an adaptive dual-domain fusion mechanism achieves a dynamic balance between denoising and structure preservation. Finally, we present a dual attention self-supervised network (DASSN), which exploits redundant information from multiple noisy observations for high-quality restoration without requiring clean reference images. Experimental results on our Wafer4 dataset show that ADF-SN achieves an average PSNR of 31.2722 dB and an a structural similarity index (SSIM) of 0.8461. This method has been deployed in wafer inspection equipment, completing the closed-loop from algorithm to application and demonstrating effectiveness, robustness, and practicality in semiconductor manufacturing.