Optical bistability in topological layered structure and its application in photonic neural network

Abstract

Optical bistable states exhibit great potentials in applications in photonic integrated circuit and photonic neural network. However, the traditional optical bistable state will be influenced by the system disorders, which are not suitable for application. In this paper, we investigate the topological bistable states in a layered structure with center inversion symmetry consisting of alternating layers of high index material TiO<sub>2</sub> and low index material SiO<sub>2</sub>. In topological mode, the electric field is highly localized in the inversion center of the layered structure (also known as the interface) and exponentially decays into the bulk. Thus, when the nonlinear permittivity is strategically introduced in those layers, nonlinear phenomena such as the bistable state appears. Finite element numerical simulations reveal the optimal bistable state appears when the layer period is 5 with a threshold power around 1.2 W/m. Benefiting from the topological feature, such bistable state persists when the random perturbations are introduced in the layer thickness and refractive index. Finally, we apply the bistable states into a photonic neural network. The bistable function shows similar prediction accuracy over a variety of learning tasks with the classic activation function Relu and sigmoid. These results suggest a novel avenue towards the insertion of the highly robust optical bistable states from topological layered structure into photonic neural network.