Optimizing the design of birefringent metasurfaces with deep learning neural networks

Metasurface presents itself as a method to create flat optical devices that generate customizable wavefronts at the nanoscale. The traditional metasurface design process involves solving Maxwell's equations through forward simulations and implementing trial-and-error to achieve the desired spectral response. This approach is computationally expensive and typically requires multiple iterations. In this study, we propose a reverse engineering solution that utilizes a deep learning artificial neural network (DNN). The ideal phase and transmission spectrums are inputted into the neural network, and the predicted dimensions which correspond to these spectrums are outputted by the network. The prediction process is less computationally expensive than forward simulations and is orders of magnitude faster to execute. Our neural network aims to identify the dimensions of elliptical nanopillars that will create the ideal phase response with a near unity transmission in a 20 nm wavelength interval surrounding the center wavelength of the spectral response. We have trained such a reverse DNN to predict the optimal dimensions for a birefringent metasurface composed of elliptical nanopillars.