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

Yi Luo^1,2,3, Deniz Mengu^1,2,3, Nezih T Yardimci^1,3

¹1Electrical and Computer Engineering Department, University of California, 420 Westwood Plaza, Los Angeles, CA 90095 USA.

Light, Science & Applications

|December 10, 2019

Summary

Researchers developed a broadband diffractive optical neural network capable of processing multiple wavelengths simultaneously. This new design enables all-optical computation using deep learning for tasks like spectral filtering and wavelength de-multiplexing.

Keywords:

Applied optics Optical techniques Other photonics

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Area of Science:

Optics and Photonics
Artificial Intelligence
Materials Science

Background:

Deep learning has driven innovation in optical computing architectures.
Diffractive optical networks merge wave optics and deep learning for optical neural networks.
Previous systems used monochromatic light, limiting their functionality.

Purpose of the Study:

To design and demonstrate a broadband diffractive optical neural network.
To enable all-optical processing of a continuum of wavelengths.
To perform tasks like spectral filtering and wavelength de-multiplexing using deep learning.

Main Methods:

Developed a broadband diffractive optical neural network architecture.
Utilized deep learning for designing multi-layer diffractive optical systems.
Fabricated and tested seven diffractive optical systems using broadband THz pulses.

Main Results:

Successfully designed and validated broadband diffractive neural networks.
Demonstrated tuneable single-passband and dual-passband spectral filters.
Achieved spatially controlled wavelength de-multiplexing using broadband light.

Conclusions:

Broadband diffractive neural networks can engineer light-matter interactions in 3D.
This approach diverges from traditional design methods for optical components.
Enables all-optical deterministic tasks and statistical inference for optical machine learning.