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.

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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

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article

Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices

Jianrong Qiu, Yalun Wang, Chenduan Chen, Dezhi Tan et al.
47 citations
Nature Communications
Neural Networks and Reservoir Computing
16.33
article

Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices

Jianrong Qiu, Yalun Wang, Chenduan Chen, Dezhi Tan, Jun Wang, Yang Zhan, Jiajia Wu, Kai Gao, Tao Wang
article en
47 citations

Abstract

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.

Nature CommunicationsVol. 15(1)
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)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Affordable and clean energy
Openalex Percentile: Top 1%
Neural Networks and Reservoir Computing
16.33
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