Rapid Design of MIM Metasurface Absorbers Based on a Two-Stage Spectral Shape Constraint

To address the complex parameter–response relationships of metal–insulator–metal (MIM) metasurface absorbers and the low design efficiency of conventional electromagnetic simulations, this study proposes a neural network-assisted design method based on a two-stage progressive spectral-shape constraint. An MIM absorber featuring composite square- and circular-ring resonators is constructed, a dataset containing 6480 samples is generated, and a forward neural-network model is developed for spectral prediction. A two-stage training strategy is adopted, in which the model first learns the spectral amplitudes and then progressively incorporates the spectral-shape constraint, thereby enhancing its ability to represent resonance peaks and valleys as well as local spectral features. The results show that the proposed model achieves a mean squared error of 2.50 × 10−6 and a mean absolute error of 1.16 × 10−3 on the test set, while reducing the spectral-shape loss by 18.98% compared with the baseline model. The model exhibits good generalization across the continuous parameter ranges, and its predictions agree closely with the results obtained from automated finite-difference time-domain simulations, while achieving an approximately 140-fold speedup over FDTD. Based on the trained model, four broadband absorber structures are designed and validated. The highest mean absorptance reaches 97.64%, and the maximum continuous high-absorption bandwidth reaches 1375 nm. The proposed data-driven method balances spectral-shape fidelity with computational efficiency and provides an effective framework for the rapid prediction and design of composite-resonant MIM metasurface absorbers.