Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)

Design of a Silicon-based Optical Neural Network

Authors
Danni Zhang, Pengfei Wang, Guangzhen Luo, Yu Bi, Ye Zhang, Junkai Yi, Yanmei Su, Yejin Zhang, Jiaoqing Pan
Corresponding Author
Jiaoqing Pan
Available Online December 2019.
DOI
10.2991/mmsta-19.2019.39How to use a DOI?
Keywords
optical neural network; simulation; SiN; chip design; neuromorphic computing
Abstract

In this paper, a silicon-based fully connected optical neural network (ONN) is designed, which can be use to image classification and recognition with accuracy greater than 97% . A fully connected neural network is constructed. One layer model has been used in chip design. Chip simulation shows accuracy could not be impacted as photons large enough. This structure could be used in deep learning.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
Series
Advances in Computer Science Research
Publication Date
December 2019
ISBN
10.2991/mmsta-19.2019.39
ISSN
2352-538X
DOI
10.2991/mmsta-19.2019.39How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Danni Zhang
AU  - Pengfei Wang
AU  - Guangzhen Luo
AU  - Yu Bi
AU  - Ye Zhang
AU  - Junkai Yi
AU  - Yanmei Su
AU  - Yejin Zhang
AU  - Jiaoqing Pan
PY  - 2019/12
DA  - 2019/12
TI  - Design of a Silicon-based Optical Neural Network
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 184
EP  - 186
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.39
DO  - 10.2991/mmsta-19.2019.39
ID  - Zhang2019/12
ER  -