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

Yi Luo; Deniz Mengu; Nezih T Yardimci; Yair Rivenson; Muhammed Veli; Mona Jarrahi; Aydogan Ozcan

doi:10.1038/s41377-019-0223-1

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

Light Sci Appl. 2019 Dec 2:8:112. doi: 10.1038/s41377-019-0223-1. eCollection 2019.

Authors

Yi Luo^#^{1

2

3}, Deniz Mengu^#^{1

2

3}, Nezih T Yardimci^{1

3}, Yair Rivenson^{1

2

3}, Muhammed Veli^{1

2

3}, Mona Jarrahi^{1

3}, Aydogan Ozcan^{1

2

3

4}

Affiliations

¹ 1Electrical and Computer Engineering Department, University of California, 420 Westwood Plaza, Los Angeles, CA 90095 USA.
² 2Bioengineering Department, University of California, Los Angeles, CA 90095 USA.
³ 3California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
⁴ 4Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA.

^# Contributed equally.

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.

Keywords: Applied optics; Optical techniques; Other photonics.