Physics Knowledge-Inspired Scattering Neural Representation for Micro-Adhesive-Spot Segmentation Under Complex Backgrounds

Segmentation of micro-adhesive spots in high-power laser packaging is challenged by morphological variability, complex backgrounds, and blurred edges, causing traditional models to fail from “feature dilution.” Inspired by physical optics knowledge, we propose a scattering neural representation framework guided by Rayleigh scattering theory. We first pretrain a denoising diffusion model, using light scattering properties, including wavelength, scattering angle, and particle number density as an inductive bias to generate high signal-to-noise ratio target features while suppressing background clutter. Subsequently, three synergistic attention modules, including an adaptive dual-attention module, an edge attention module, and a small object enhancement module, refine target features by dynamically expanding the receptive field, sharpening boundaries, and enhancing microtarget responses. Extensive experiments on proprietary and public datasets demonstrate that our model significantly outperforms state-of-the-art methods in precise segmentation and background interference suppression. This work translates physical insights into architectural advantages, establishing an efficient and interpretable paradigm for addressing the persistent challenge of industrial small object segmentation.