Share Email Print
cover

Proceedings Paper

Integration of diffractive optical neural networks with electronic neural networks (Conference Presentation)
Author(s): Aydogan Ozcan; Deniz Mengu; Yi Luo; Yair Rivenson; Jingxi Li

Paper Abstract

We demonstrate significant improvements in the inference accuracy of diffractive optical neural networks and report that a five-layer, phase-only (or amplitude/phase) modulation diffractive network can achieve 97.18% (97.81%) and 89.13% (89.32%) blind-testing accuracy for MNIST and Fashion-MNIST datasets, respectively. Moreover, the integration of diffractive neural networks with electronic deep neural networks is investigated. Using a single fully-connected layer on the electronic part and a five-layer, phase-only diffractive neural network at the optical front-end, we achieved blind-testing accuracies of 98.71% and 90.04% for MNIST and Fashion-MNIST datasets, respectively, despite a >7.8-fold reduction in the number of pixels at the opto-electronic sensor-array.

Paper Details

Date Published: 9 March 2020
PDF
Proc. SPIE 11284, Smart Photonic and Optoelectronic Integrated Circuits XXII, 112841X (9 March 2020); doi: 10.1117/12.2547200
Show Author Affiliations
Aydogan Ozcan, Univ. of California, Los Angeles (United States)
Deniz Mengu, Univ. of California, Los Angeles (United States)
Yi Luo, Univ. of California, Los Angeles (United States)
Yair Rivenson, Univ. of California, Los Angeles (United States)
Jingxi Li, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11284:
Smart Photonic and Optoelectronic Integrated Circuits XXII
Sailing He; Laurent Vivien, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray