Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2

Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions. However, common strategies, including the integration of external electrode arrays or photonic structures with 2D materials, and barely patterned 2D materials, exhibit a contradiction between performance and tunability. Because the unique band dispersions of 2D materials can provide hidden paths to boost nonlinear responses independently, here we introduced a new free-space optical computing concept within a bare molybdenum disulfide array. This system can preserve high modulation performance with fast speed, low energy consumption, and high signal-to-noise ratio. Due to the freedom from the restrictions of fixed photonic structures, the tunability is also enhanced through the synergistic encodings of the 2D cells and the excitation pulses. The computing mechanism of transition from two-photon absorption to synergistic excited states absorption intrinsically improved the modulation capability of nonlinear optical responses, revealed from the relative transmittance modulated by a pump-probe-control strategy. Optical artificial neural network (ANN) and digital processing were demonstrated, revealing the feasibility of the free-space optical computing based on bare 2D materials toward neuromorphic applications.

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Publication Details

Journal
Nature Communications
Published
2024-11-27
DOI
https://doi.org/10.1038/s41467-024-54776-z
Citations
24
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
8.34

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article

Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2

Ping Wang, Jinshui Miao, Wenhao Shi, Wei Ju et al.
24 citations
Nature Communications
Neural Networks and Reservoir Computing
8.34
article

Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2

Ping Wang, Jinshui Miao, Wenhao Shi, Wei Ju, Yali Bi, Jianbin Xu, Kai Peng, Kan‐Hao Xue, Lei Tong, Zhuiri Peng, Langlang Xu, Runfeng Lin, Deep Jariwala, Wei Bao, Qiang He, Xiangxiang Yu, Lei Ye, Yu Hui, Zheng Li, Xinliang Zhang, Xiangshui Miao, Yilun Wang, Ming Tang, Xinyu Huang, Huajun Sun
article en
24 citations

Abstract

Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions. However, common strategies, including the integration of external electrode arrays or photonic structures with 2D materials, and barely patterned 2D materials, exhibit a contradiction between performance and tunability. Because the unique band dispersions of 2D materials can provide hidden paths to boost nonlinear responses independently, here we introduced a new free-space optical computing concept within a bare molybdenum disulfide array. This system can preserve high modulation performance with fast speed, low energy consumption, and high signal-to-noise ratio. Due to the freedom from the restrictions of fixed photonic structures, the tunability is also enhanced through the synergistic encodings of the 2D cells and the excitation pulses. The computing mechanism of transition from two-photon absorption to synergistic excited states absorption intrinsically improved the modulation capability of nonlinear optical responses, revealed from the relative transmittance modulated by a pump-probe-control strategy. Optical artificial neural network (ANN) and digital processing were demonstrated, revealing the feasibility of the free-space optical computing based on bare 2D materials toward neuromorphic applications.

Nature CommunicationsVol. 15(1)
Hefei University of Technology (CN), Rensselaer Polytechnic Institute (US), Chinese University of Hong Kong (HK), Huazhong Agricultural University (CN), Shanghai Institute of Technical Physics (CN), Wuhan National Laboratory for Optoelectronics (CN), Huazhong University of Science and Technology (CN), University of Pennsylvania (US)
National Science Foundation, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Wuhan National Laboratory for Optoelectronics, Division of Materials Research
Affordable and clean energy
Openalex Percentile: Top 2%
Neural Networks and Reservoir Computing
8.34
DOIOpen Access PDFOpenAlex
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