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

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

Article

- Design of task-specific optical systems using broadband diffractive neural networks
- Light: Science & Applications Vol. 8, Issue 6, Pages: 1084-1097(2019)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, 420 Westwood Plaza, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
  4.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-019-0223-1
  CLC：
- Published：2019，
  
  Published Online：02 December 2019，
  
  Received：04 October 2019，
  
  Revised：08 November 2019，
  
  Accepted：15 November 2019
- Accepted：
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Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light: Science & Applications, 8, 1084-1097 (2019).
DOI：

Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light: Science & Applications, 8, 1084-1097 (2019). DOI： 10.1038/s41377-019-0223-1.

摘要

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 lightɃmatter 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.

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