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Intelligent neuromorphic computing based on nanophotonics and metamaterials

  • Multifunctional Nanoscale Optics and Metamaterials Prospective
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Abstract

With the rapid development of artificial intelligence and computing chips approaching the bottleneck of power consumption and computing power, the research on intelligent computing hardware with high speed and high energy efficiency is an important trend. Recently, neuromorphic computing represented by photonic circuit neural networks and all-optical diffraction neural networks has attracted widespread attention due to their ultra-fast and ultra-efficient computing architectures. In this perspective, we first review some representative works and introduce them through two main lines of planar photonic circuit neural networks and three-dimensional diffraction neural networks to compare their characteristics and performance. We further discuss programmable designs for neuromorphic computing hardware, which bring it closer to general-purpose computing devices. Besides intelligent neural networks in the optical band, we also review the development and application of the diffractive neural networks in the microwave band, showing their programmable capabilities. Finally, we present the future directions and development trends of intelligent neuromorphic computing and its potential applications in wireless communications, information processing, and sensing.

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Data availability

The data that support the findings of this study are available on request from the corresponding authors.

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Funding

The work is supported by the National Natural Science Foundation of China (62288101 and 92167202), the National Key Research and Development Program of China (2022YFA1404903, 2017YFA0700201, 2017YFA0700202, and 2017YFA0700203), the Major Project of Natural Science Foundation of Jiangsu Province (BK20212002), the State Key Laboratory of Millimeter Waves, Southeast University, China (K201924), the Fundamental Research Funds for the Central Universities (2242023K5002, 2242018R30001, 2242022R20017), the 111 Project (111-2-05), and the China Postdoctoral Science Foundation (2242023K5002, 2021M700761, 2022T150112), Young Elite Scientists Sponsorship Program by CAST (2022QNRC001).

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Author notes

  1. Qian Ma and Xinxin Gao have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China

    Qian Ma, Xinxin Gao, Ze Gu, Che Liu, Jian Wei You & Tie Jun Cui

  2. Institute of Electromagnetic Space, Southeast University, Nanjing, 210096, China

    Qian Ma, Ze Gu, Che Liu, Jian Wei You & Tie Jun Cui

  3. State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, SAR, 999077, China

    Xinxin Gao

  4. School of Electronic Engineering and Computer Sciences, Peking University, Beijing, 100871, China

    Lianlin Li

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  1. Qian Ma
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Contributions

T. J. Cui and Q. Ma initiated the plan and supervised the entire study. Q. Ma, X. Gao, and Z. Gu write the main part of the manuscript. L. Li, J. W. You, C. Liu, and T. J. Cui carried out the modifications and inspections. Q. Ma, X. Gao, Z. Gu, and T. J. Cui prepared the final manuscript with input from all authors. All authors discussed the research.

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Correspondence to Tie Jun Cui.

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Ma, Q., Gao, X., Gu, Z. et al. Intelligent neuromorphic computing based on nanophotonics and metamaterials. MRS Communications (2024). https://doi.org/10.1557/s43579-024-00520-z

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