Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra

• URL: http://arxiv.org/abs/2608.11860v1
• Date: Wed, 12 Aug 2026 09:47:06 GMT
• Title: Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra
• Abstract summary: Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses.<n>This work presents a two-stage deformable-convolutional framework for reconstructing metal--insulator--metal resonator from 80-dimensional absorption spectra.
• Score: 2.4149105714758545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine geometric features. This work presents a two-stage deformable-convolutional framework for reconstructing metal--insulator--metal resonator geometries from 80-dimensional absorption spectra. The spectrum is projected to a $150\times4\times4$ latent representation and decoded into a $64\times64$ resonator mask. Training combines supervised reconstruction with least-squares adversarial refinement initialized from the best supervised checkpoint. A three-run ablation compares deformable convolution with plain convolution, involution, Dynamic Conv, and ODConv under the same architecture. The proposed model achieves $20.79\pm0.31$~dB PSNR and $0.8501\pm0.0082$ SSIM, improving over plain convolution by 2.16~dB and 0.0831, respectively. It further achieves Dice $0.9623\pm0.0027$, IoU $0.9342\pm0.0038$, and boundary F-score $0.9550\pm0.0027$. Spectral consistency evaluated using a frozen forward surrogate yields RMSE $0.0805\pm0.0013$ and $R^2=0.7923\pm0.0065$. Learned offsets show stronger adaptive sampling at coarse and intermediate decoder stages. Overall, deformable sampling with supervised initialization and adversarial refinement improves spectrum-conditioned geometry reconstruction.

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