A Transformer-Based Deep Learning Method for Inverse Design of Electromagnetically Induced Transparency Metasurfaces

In this work, we propose a novel transformer-based deep learning model for the design of electromagnetically induced transparency (EIT) metasurfaces, which consists of a forward network to predict transmission spectra from structural parameters and an inverse network to retrieve structural parameters from target spectra. To train the model, we generated a dataset of 23,500 samples by automating CST simulations. The well-trained model can predict all seven structural parameters of an EIT metasurface from a given target spectrum within milliseconds, achieving a mean square error (MSE) of 8.49 × 10−4 at convergence. The mean errors between the predicted data and target parameters remain below 0.25 μm. The relative spectral error (RSE) is employed to evaluate the discrepancy between the spectra from predicted structures and the targets, with a maximum RSE of 0.57%. Benchmarking against two other neural networks confirms the superior predictive capability and accuracy of our model. Furthermore, the method not only streamlines EIT metasurface design but is readily adaptable to diverse metasurface devices across the electromagnetic spectrum, establishing a versatile platform for metasurface inverse design.