Universal deep learning representation of effective refractive index for photonics channel waveguides

Abstract

An optical waveguide is the fundamental element in a photonic integrated circuit. This paper establishes a universal deep learning representation for the effective refractive index of an optical channel waveguide for the entire and usual parameter space for applications in photonics. The deep learning model is able to make precise predictions for wide spectrum optical wavelengths, dielectric materials of refractive indices varying from 1.45 to 3.8, and a wide range of feasible geometrical parameters of the waveguides. The deep learning model consists of fully connected feedforward neural networks, and rigorous optimization of neural network architecture is carried out. Deep learning models with two and three hidden layers provide rapid convergence with a minimal number of training data points and offer unprecedented precisions that are a few orders better in magnitude than the conventional interpolation techniques.