拓扑层结构中的光学双稳态及其在光神经网络中的应用

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

具有光学双稳态的非线性微纳光学器件在光集成电路和光神经网络中有着重要的应用.然而,传统光学双稳态特性会受到内部系统无序和外部系统扰动的影响,不利于应用.本文利用SiO_(2)和TiO_(2)的拓扑多层薄膜结构,在拓扑模式电场强度最大的区域引入非线性介电常数,探究拓扑模式的光学双稳态特性,并分析了双稳态特性在层厚度变化和折射率变化下的鲁棒性.最后,本文将双稳态特性作为非线性激活函数应用到了光神经网络计算中,相比于传统的非线性激活函数ReLu和Sigmoid,双稳态非线性激活函数展示出了更好的图像识别精度.本文结果对研发下一代鲁棒性强的光神经网络有着重要研究意义.

Optical bistable state exhibits its great potential 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 work,we investigate the topological bistable states in a layered structure with center inversion symmetry consisting of alternating layers of high index material TiO_(2) and low index material SiO_(2).In the 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 into those layers,nonlinear phenomena such as the bistable state appears.The finite element numerical simulations reveal that the optimal bistable state appears when the layer period is 5 with a threshold power around 1.2 W/m.Benefiting from the topological characteristics,such a bistable state persists when random perturbations are introduced into the layer thickness and refractive index.Finally,we apply the bistable states to a photonic neural network.The bistable function shows prediction accuracy similar to the classic activation function ReLu and sigmoid in various learning tasks.These results provide a novel method for inserting highly robust optical bistable states from topological layered structure into photonic neural network.