SFMambaSR: A spatial-frequency enhanced Mamba network for wafer image super-resolution.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 42700679.
- Also identified by DOI 10.1016/j.neunet.2026.109570.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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.