Deep inverse modeling the near field response of optical metasurfaces

In this work, we investigate the problem of real-Time design of electromagnetic (EM) metamaterials to achieve custom scattering properties: A type of inverse modeling problem. To address this problem, we investigate a class of DNN-based models that are specially designed to address inverse problems, termed deep inverse models (DIMs). DIMs have recently shown tremendous promise for solving material design problems, however, relatively less work has been done for high-dimensional problems, such as near-field design. In this work, we performed 1500 simulations of a metasurface with a 3x3 array of meta-Atom pillars, where we independently and randomly-varied the radii of each pillar and recorded the resulting electric near-field values. We then used this dataset to train and evaluate several data-driven inverse models, including several variations of a recently-successful DIM, termed the Tandem. Our results indicate that the Tandem is capable of making relatively accurate design predictions in this challenging high-dimensional settings, and doing so in real-Time (e.g., roughly 4ms). We find that the choice of model architecture significantly impacts the accuracy of the inverse model, and even higher accuracy can be achieved with further improvements to the Tandem's design.