Scale-, shift-, and rotation-invariant diffractive optical networks

Abstract

A method of forming an optical neural network for processing an input object image or optical signal that is invariant to object transformations includes training a software-based neural network model to perform one or more specific optical functions for a multi-layer optical network having physical features located in each of the layers of the optical neural network. The training includes feeding different input object images or optical signals that have random transformations or shifts and computing at least one optical output of optical transmission and/or reflection through the optical neural network using an optical wave propagation model and iteratively adjusting transmission/reflection coefficients for each layer until optimized transmission/reflection coefficients are obtained. A physical embodiment of the optical neural network is then made that has a plurality of substrate layers having physical features that match the optimized transmission/reflection coefficients obtained by the trained neural network model.