All-optical machine learning using diffractive deep neural networks-Reference-Cited by-同舟云学术

All-optical machine learning using diffractive deep neural networks

Published:2018-09-07 Issue:6406 Volume:361 Page:1004-1008
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Lin Xing¹²³^ORCID,Rivenson Yair¹²³,Yardimci Nezih T.¹³^ORCID,Veli Muhammed¹²³,Luo Yi¹²³,Jarrahi Mona¹³,Ozcan Aydogan¹²³⁴^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA.

2. Department of Bioengineering, University of California, Los Angeles, CA 90095, USA.

3. California NanoSystems Institute (CNSI), 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.

Abstract

All-optical deep learning Deep learning uses multilayered artificial neural networks to learn digitally from large datasets. It then performs advanced identification and classification tasks. To date, these multilayered neural networks have been implemented on a computer. Lin et al. demonstrate all-optical machine learning that uses passive optical components that can be patterned and fabricated with 3D-printing. Their hardware approach comprises stacked layers of diffractive optical elements analogous to an artificial neural network that can be trained to execute complex functions at the speed of light. Science , this issue p. 1004

Funder

National Science Foundation

Howard Hughes Medical Institute

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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