Fourier-space Diffractive Deep Neural Network

Tao Yan, Jiamin Wu, Tiankuang Zhou, Hao Xie, Feng Xu, Jingtao Fan, Lu Fang, Xing Lin, and Qionghai Dai
Phys. Rev. Lett. 123, 023901 – Published 9 July 2019
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Abstract

In this Letter we propose the Fourier-space diffractive deep neural network (FDNN2) for all-optical image processing that performs advanced computer vision tasks at the speed of light. The FDNN2 is achieved by placing the extremely compact diffractive modulation layers at the Fourier plane or both Fourier and imaging planes of an optical system, where the optical nonlinearity is introduced from ferroelectric thin films. We demonstrated that FDNN2 can be trained with deep learning algorithms for all-optical saliency detection and high-accuracy object classification.

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  • Received 2 April 2019
  • Revised 6 June 2019

DOI:https://doi.org/10.1103/PhysRevLett.123.023901

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsPlasma PhysicsNonlinear DynamicsAtomic, Molecular & OpticalGeneral PhysicsNetworksCondensed Matter, Materials & Applied Physics

Authors & Affiliations

Tao Yan1,*, Jiamin Wu1,*, Tiankuang Zhou1,2,*, Hao Xie1, Feng Xu3, Jingtao Fan1, Lu Fang2, Xing Lin1,4,†, and Qionghai Dai1,‡

  • 1Department of Automation, Tsinghua University, Beijing, 100084, People’s Republic of China
  • 2Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People’s Republic of China
  • 3School of Software, Tsinghua University, Beijing 100084, People’s Republic of China
  • 4Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095, USA

  • *T. Y., J. W. and T. Z. contributed equally to this Letter.
  • linx2019@tsinghua.edu.cn
  • qhdai@tsinghua.edu.cn

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Issue

Vol. 123, Iss. 2 — 12 July 2019

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