Optical activation function using a metamaterial waveguide for an all-optical neural network

In this study, we experimentally demonstrated that the nonlinear optical coefficient of the original Si can be enhanced by incorporating a metamaterial structure into an existing silicon waveguide. The two-photon absorption coefficient enhanced by the metamaterial structure was 424 cm/GW, which is 1.2 × 10<sup>3</sup> times higher than that of Si. Using this metamaterial waveguide-based nonlinear optical activation function, we achieved a high inference accuracy of 98.36% in the handwritten character recognition task, comparable to that obtained with the ReLU function as the activation function. Therefore, our approach can contribute to the realization of more power-efficient and compact all-optical neural networks.

Authors

Institutions

Publication Details

Journal
Optics Letters
Published
2024-09-23
DOI
https://doi.org/10.1364/ol.540234
Citations
3
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optical activation function using a metamaterial waveguide for an all-optical neural network

Tomohiro Amemiya, Yuya Shoji, Yoshihiro Honda
3 citations
Optics Letters
Neural Networks and Reservoir Computing
1.04
article

Optical activation function using a metamaterial waveguide for an all-optical neural network

Tomohiro Amemiya, Yuya Shoji, Yoshihiro Honda
article en
3 citations

Abstract

In this study, we experimentally demonstrated that the nonlinear optical coefficient of the original Si can be enhanced by incorporating a metamaterial structure into an existing silicon waveguide. The two-photon absorption coefficient enhanced by the metamaterial structure was 424 cm/GW, which is 1.2 × 10<sup>3</sup> times higher than that of Si. Using this metamaterial waveguide-based nonlinear optical activation function, we achieved a high inference accuracy of 98.36% in the handwritten character recognition task, comparable to that obtained with the ReLU function as the activation function. Therefore, our approach can contribute to the realization of more power-efficient and compact all-optical neural networks.

Optics LettersVol. 49(20)
Sony Corporation (United States) (US), Shanghai Institute for Science of Science (CN), Institute of Science Tokyo (JP)
Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology, Adaptable and Seamless Technology Transfer Program through Target-Driven R and D
Openalex Percentile: Top 19%
Neural Networks and Reservoir Computing
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Optical activation function using a metamaterial waveguide for an all-optical neural network — Tomohiro Amemiya, Yuya Shoji, et al. · Optics Letters (2024) | TGRS Research Map | TGRS