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
- Weiwei Zhao (ORCID: https://orcid.org/0000-0002-8179-4775)
- Xingyuan Xu (ORCID: https://orcid.org/0000-0002-8190-4700)
- Francesco Da Ros (ORCID: https://orcid.org/0000-0002-9068-9125)
- Yunhong Ding (ORCID: https://orcid.org/0000-0002-6823-4722)
- Kun Xu (ORCID: https://orcid.org/0000-0002-1663-9998)
- Siqi Yan (ORCID: https://orcid.org/0000-0002-5905-3006)
- Jiawang Xiong (ORCID: https://orcid.org/0000-0002-4695-6169)
- Wenyi Peng
- Ting Jiang (ORCID: https://orcid.org/0000-0003-3598-3804)
- Chen Liu (ORCID: https://orcid.org/0000-0003-2000-8133)
- Chi Zhang (ORCID: https://orcid.org/0000-0002-1324-2311)
- Ming Tang (ORCID: https://orcid.org/0000-0001-8669-4186)
- Hao Wu (ORCID: https://orcid.org/0000-0002-7829-4673)
- Jingze Liu (ORCID: https://orcid.org/0000-0002-5468-5266)
- Ziwen Zhou (ORCID: https://orcid.org/0009-0006-7782-4595)
Institutions
- 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)
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
- National Natural Science Foundation of China