Ultrafast Silicon/Graphene Optical Nonlinear Activator for Neuromorphic Computing

Abstract Optical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks. However, the absence of high‐speed, energy‐efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like real‐time intelligent signal processing. In this work, a novel silicon/graphene ultrafast all‐optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene is demonstrated. Exploiting the exceptional picosecond‐scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to ≈93.6 ps, establishing all‐optical activator as the fastest known in silicon photonics to knowledge. Moreover, the all‐optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Its feasibility and capability for use in ONNs, manifesting performance comparable with commonly used activation functions are experimentally confirmed. This breakthrough in speed and energy efficiency of all‐optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs.

Authors

Institutions

Publication Details

Journal
Advanced Optical Materials
Published
2024-10-03
DOI
https://doi.org/10.1002/adom.202401686
Citations
4
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.39

Funders

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

Ultrafast Silicon/Graphene Optical Nonlinear Activator for Neuromorphic Computing

Weiwei Zhao, Xingyuan Xu, Francesco Da Ros, Yunhong Ding et al.
4 citations
Advanced Optical Materials
Neural Networks and Reservoir Computing
1.39
article

Ultrafast Silicon/Graphene Optical Nonlinear Activator for Neuromorphic Computing

Weiwei Zhao, Xingyuan Xu, Francesco Da Ros, Yunhong Ding, Kun Xu, Siqi Yan, Jiawang Xiong, Wenyi Peng, Ting Jiang, Chen Liu, Chi Zhang, Ming Tang, Hao Wu, Jingze Liu, Ziwen Zhou
article en
4 citations

Abstract

Abstract Optical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks. However, the absence of high‐speed, energy‐efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like real‐time intelligent signal processing. In this work, a novel silicon/graphene ultrafast all‐optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene is demonstrated. Exploiting the exceptional picosecond‐scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to ≈93.6 ps, establishing all‐optical activator as the fastest known in silicon photonics to knowledge. Moreover, the all‐optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Its feasibility and capability for use in ONNs, manifesting performance comparable with commonly used activation functions are experimentally confirmed. This breakthrough in speed and energy efficiency of all‐optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs.

Advanced Optical MaterialsVol. 12(34)
Beijing University of Posts and Telecommunications (CN), Wuhan National Laboratory for Optoelectronics (CN), Huazhong University of Science and Technology (CN), Technical University of Denmark (DK)
National Natural Science Foundation of China
Openalex Percentile: Top 15%
Neural Networks and Reservoir Computing
1.39
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.