Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices
All-optical nonlinear activation functions (NAFs) are crucial for enabling rapid optical neural networks (ONNs). As linear matrix computation advances in integrated ONNs, on-chip all-optical NAFs face challenges such as limited integration, high latency, substantial power consumption, and a high activation threshold. In this work, we develop an integrated nonlinear optical activator based on the butt-coupling integration of two-dimensional (2D) MoTe<sub>2</sub> and optical waveguides (OWGs). The activator exhibits an ultra-broadband response from visible to near-infrared wavelength, a low activation threshold of 0.94 μW, a small device size (~50 µm<sup>2</sup>), an ultra-fast response rate (2.08 THz), and high-density integration. The excellent nonlinear effects and broadband response of 2D materials have been utilized to create all-optical NAFs. These activators were applied to simulate MNIST handwritten digit recognition, achieving an accuracy of 97.6%. The results underscore the potential application of this approach in ONNs. Moreover, the classification of more intricate CIFAR-10 images demonstrated a generalizable accuracy of 94.6%. The present nonlinear activator promises a general platform for three-dimensional (3D) ultra-broadband ONNs with dense integration and low activation thresholds by integrating a variety of strong nonlinear optical (NLO) materials (e.g., 2D materials) and OWGs in glass.
Authors
- Jianrong Qiu (ORCID: https://orcid.org/0000-0003-3148-2500)
- Yalun Wang
- Chenduan Chen
- Dezhi Tan (ORCID: https://orcid.org/0000-0002-2217-0583)
- Jun Wang (ORCID: https://orcid.org/0000-0003-0064-3551)
- Yang Zhan (ORCID: https://orcid.org/0000-0002-7522-8750)
- Jiajia Wu (ORCID: https://orcid.org/0009-0006-6017-037X)
- Kai Gao (ORCID: https://orcid.org/0000-0002-4716-0680)
- Tao Wang (ORCID: https://orcid.org/0009-0001-2024-0920)
Institutions
- Chinese Academy of Sciences (CN)
- Zhejiang Lab (CN)
- Shanghai Institute of Optics and Fine Mechanics (CN)
- Communication University of Zhejiang (CN)
- University of Chinese Academy of Sciences (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2024-10-19
- DOI
- https://doi.org/10.1038/s41467-024-53371-6
- Citations
- 47
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 16.33
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation