All-optical information-processing capacity of diffractive surfaces

Onur Kulce; Deniz Mengu; Yair Rivenson; Aydogan Ozcan

doi:10.1038/s41377-020-00439-9

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All-optical information-processing capacity of diffractive surfaces

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- All-optical information-processing capacity of diffractive surfaces
- Light: Science & Applications Vol. 10, Issue 2, Pages: 217-233(2021)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, 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
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-020-00439-9
  CLC：
- Published：2021，
  
  Published Online：28 January 2021，
  
  Received：11 August 2020，
  
  Revised：16 November 2020，
  
  Accepted：17 November 2020
- Accepted：
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Kulce, O. et al. All-optical information-processing capacity of diffractive surfaces. Light: Science & Applications, 10, 217-233 (2021).
DOI：

Kulce, O. et al. All-optical information-processing capacity of diffractive surfaces. Light: Science & Applications, 10, 217-233 (2021). DOI： 10.1038/s41377-020-00439-9.

摘要

Abstract

The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances related to the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine-learning tasks through light–matter interactions and diffraction. Here

we analyze the information-processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view. We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network

up to a limit that is dictated by the extent of the input and output fields-of-view. Deeper diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view and exhibit depth advantages in terms of their statistical inference

learning

and generalization capabilities for different image classification tasks when compared with a single trainable diffractive surface. These analyses and conclusions are broadly applicable to various forms of diffractive surfaces

including

e.g.

plasmonic and/or dielectric-based metasurfaces and flat optics

which can be used to form all-optical processors.

关键词

Keywords

references

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