SFMambaSR: A spatial-frequency enhanced Mamba network for wafer image super-resolution

Wafer defect inspection is crucial for yield and reliability, but shrinking defect sizes demand higher imaging resolution. While high-magnification optics provide resolution, their narrow field of view limits inspection efficiency. To balance precision and throughput, we propose a solution that reconstructs high-resolution wafer images from large-field low-magnification captures via a super-resolution algorithm. This method can improve detection efficiency without affecting accuracy. We design a dual-domain fusion lightweight SR network (SFMambaSR) specifically for wafer microscopy images. In the spatial domain, a Visual State Space Model (VSSM) and Multi-Scale Feature Extraction (MSFE) module jointly fuse global and local representations, while in the frequency domain, a wavelet-based Frequency-Domain Transformation (FDT) module enhances high-frequency defect details. Experiments on our large-scale wafer microscopy dataset demonstrate that SFMambaSR achieves the best PSNR and competitive or best SSIM across 2 × , 3 × , and 4 ×  upscaling factors, while using only 807K parameters.