Deep Learning Enabled Bandpass Electromagnetic Metasurface Inverse Design

Conditional variational autoencoders (cVAE) is proposed to automate and optimize the design of band-pass electromagnetic metasurfaces. Electromagnetic responses curves and metasurface structural parameters are used as the input to train the deep neural work based cVAE model. The model is trained on ten thousand metasurface and transmission curve pairs, which are calculated using MIT Electromagnetic Equation Propagation (MEEP) software. The trained model takes transmission curves as input and then output metasurface structures, achieving a deep learning enabled inverse design process. By comparing the input transmission curve with the MEEP simulated curves of the generated metasurface, it is found that the model has high accuracy, giving a mean square error (MSE) value of 0.005 and R2 score of 0.967. The metasurface structures generated by cVAE also show good diversity, indicating that the cVAE captures the effective features in the latent space. Using the cVAE model, metasurface could be generated in less than one second using a single graphics processing unit (GPU), paving the way for fast, efficient and scalable inverse design solutions in the field of electromagnetic metasurfaces.