Optical Learning Neural Network Using Photorefractive Waveguides

Abstract

A model of an optical neural network with learning ability is proposed. We numerically evaluate the learning ability of the proposed network by using parameters determined by experiments. Adaptive connections between artificial neurons are implemented using photorefractive (PR) waveguides that can be optically modified by guided beams. The network consists of three layers and has bipolar weights within the limited range. The bipolar weight is encoded as the difference between optical power transmittances of signal beams in two channels of the PR waveguides. The adaptivity of the transmittance of PR waveguide is experimentally evaluated and is incorporated into the proposed network simulated in a computer. The proposed network is trained by a simplified local learning algorithm. Numerical results showed that the proposed three-layered network with six hidden neurons can solve the exclusive-or problem.