Design of task-specific optical systems using broadband diffractive neural networks

TLDR

Deep learning has spurred the development of optical computing, and diffractive optical networks merge wave optics with deep‑learning to create all‑optical neural networks, but prior work relied on monochromatic coherent light for tasks such as handwritten‑digit recognition. This study aims to design a broadband diffractive optical neural network that can process a continuum of wavelengths from a temporally incoherent broadband source to perform a specific deep‑learning‑learned task all‑optically. The authors designed, fabricated, and tested seven multilayer diffractive optical systems that transform the wavefront of a broadband THz pulse into tunable single‑ and dual‑passband spectral filters and spatially controlled wavelength demultiplexers. Experimental results confirm that merging material dispersion with deep‑learning design enables task‑specific optical components that engineer light‑matter interaction in 3D, achieving deterministic filtering and wavelength demultiplexing beyond conventional analytical methods.

Abstract

Deep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tuneable, single-passband and dual-passband spectral filters and (2) spatially controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy, broadband diffractive neural networks help us engineer the light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.