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  • Received: May. 6, 2019

    Accepted: Jun. 5, 2019

    Posted: Jul. 3, 2019

    Published Online: Jul. 3, 2019

    The Author Email: Zongfu Yu (zyu54@wisc.edu)

    DOI: 10.1364/PRJ.7.000823

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    Erfan Khoram, Ang Chen, Dianjing Liu, Lei Ying, Qiqi Wang, Ming Yuan, Zongfu Yu. Nanophotonic media for artificial neural inference[J]. Photonics Research, 2019, 7(8): 08000823

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Photonics Research, Vol. 7, Issue 8, 08000823 (2019)

Nanophotonic media for artificial neural inference 

Erfan Khoram1, Ang Chen1, Dianjing Liu1, Lei Ying1, Qiqi Wang2, Ming Yuan3, and Zongfu Yu1,*

Author Affiliations

  • 1Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, Wisconsin 53706, USA
  • 2Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 3Department of Statistics, Columbia University, New York, New York 10027, USA

Abstract

We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Complex information is encoded in the wavefront of an input light. The medium transforms the wavefront to realize sophisticated computing tasks such as image recognition. At the output, the optical energy is concentrated in well-defined locations, which, for example, can be interpreted as the identity of the object in the image. These computing media can be as small as tens of wavelengths and offer ultra-high computing density. They exploit subwavelength scatterers to realize complex input/output mapping beyond the capabilities of traditional nanophotonic devices.

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