Terahertz applications of diffractive optical networks

Abstract

We introduce a physical mechanism to perform machine learning by demonstrating a Diffractive Deep Neural Network (D2NN) architecture that can all-optically implement various functions following the deep learning-based design of passive layers that work collectively. We created 3D-printed diffractive networks that implement all-optical classification of images of handwritten digits and fashion products as well as the function of an imaging lens, spectral filters and wavelength demultiplexers at terahertz part of the spectrum. This passive diffractive network framework is broadly applicable to different parts of the electromagnetic spectrum, and can perform at the speed of light various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using diffractive optical networks.