Topology-preserving deep learning for structural integrity in optical semiconductor characterization at deeply subwavelength resolution

Abstract

As semiconductor devices continue to shrink to the nanoscale, ensuring their structural accuracy becomes increasingly critical. Optical imaging techniques play a key role in defect detection in semiconductor manufacturing. However, these optical techniques are fundamentally limited by the diffraction limit, which restricts the resolution of features smaller than half the illumination wavelength. While deep learning has shown promise in pushing beyond this limit, conventional models often rely on pixel-wise loss functions that fail to capture the global structure of semiconductor patterns. This can result in broken lines, missing features, and inaccurate defect detection. In this work, we introduce a topology-preserving deep learning framework tailored for high-resolution optical imaging in semiconductor characterization. By embedding topological constraints through persistent homology in a differentiable loss function, our model enforces structural consistency across the entire image. Experimental results demonstrate an improvement of nearly 30% in the quantifier metric against ground truth compared to traditional methods on the nano-particle dataset, accurately resolving features as small as 0.16λ (100 nm) under 640 nm illumination. The model further reduces false disconnectivities by 5% in nano line localization. This topologically-aware approach provides a robust and non-destructive solution for subwavelength imaging in semiconductor metrology, enabling more reliable inspection in precision manufacturing.