Meshless optical mode solving using scalable deep deconvolutional neural network

Abstract

Optical mode solving is of paramount importance in photonic design and discovery. In this paper we propose a deep deconvolutional neural network architecture for a meshless, and resolution scalable optical mode calculations. The solution is arbitrary in wavelengths and applicable for a wide range of photonic materials and dimensions. The deconvolutional model consists of two stages: the first stage projects the photonic geometrical parameters to a vector in a higher dimensional space, and the second stage deconvolves the vector into a mode image with the help of scaling blocks. Scaling block can be added or subtracted as per desired resolution in the final mode image, and it can be effectively trained using a transfer learning approach. Being a deep learning model, it is light, portable, and capable of rapidly disseminating edge computing ready solutions. Without the loss of generality, we illustrate the method for an optical channel waveguide, and readily generalizable for wide range photonic components including photonic crystals, optical cavities and metasurfaces.