Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks

TL;DR

This paper introduces a broadband diffractive optical neural network that processes multiple wavelengths simultaneously for optical filtering and wavelength de-multiplexing, validated through experimental fabrication and testing.

Contribution

It presents a novel deep learning-based design approach for broadband diffractive optical systems that operate across a continuum of wavelengths, enabling new task-specific optical functionalities.

Findings

Successfully designed and tested seven multi-layer diffractive optical systems.

Demonstrated tunable and dual passband spectral filtering capabilities.

Achieved spatially-controlled wavelength de-multiplexing.

Abstract

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 tunable, single passband as well as 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 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.