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

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 150×4×4 latent representation and decoded into a 64×64 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±0.31~dB PSNR and 0.8501±0.0082 SSIM, improving over plain convolution by 2.16~dB and 0.0831, respectively. It further achieves Dice 0.9623±0.0027, IoU 0.9342±0.0038, and boundary F-score 0.9550±0.0027. Spectral consistency evaluated using a frozen forward surrogate yields RMSE 0.0805±0.0013 and R2=0.7923±0.0065. 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.