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arXiv · 2510.21924

Inverse Design of Metasurface for Spectral Imaging

Rongzhou Chen·Haitao Nie·Shuo Zhu·Yaping Zhao·Chutian Wang·Edmund Y. Lam
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Abstract

Inverse design of metasurfaces for the joint optimization of optical modulation and algorithmic decoding in computational optics presents significant challenges, especially in applications such as hyperspectral imaging. We introduce a physics-data co-driven framework for designing reconfigurable metasurfaces fabricated from the phase-change material Ge2Sb2Se4Te1 to achieve compact, compressive spectral imaging in the shortwave infrared region. Central to our approach is a differentiable neural simulator, trained on over 320,000 simulated geometries, that accurately predicts spectral responses across 11 crystallization states. This differentiability enables end-to-end joint optimization of the metasurface geometry, its spectral encoding function, and a deep reconstruction network. We also propose a soft shape regularization technique that preserves manufacturability during gradient-based updates. Experiments show that our optimized system improves reconstruction fidelity by up to 7.6 dB in the peak-signal-to-noise ratio, with enhanced noise resilience and improved measurement matrix conditioning, underscoring the potential of our approach for high-performance hyperspectral imaging.

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Rongzhou Chen, Haitao Nie, Shuo Zhu, Yaping Zhao, Chutian Wang, Edmund Y. Lam. 2025-10-24. Inverse Design of Metasurface for Spectral Imaging. https://arxiv.org/abs/2510.21924

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