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Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing

Author

Listed:
  • See-On Park

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Hakcheon Jeong

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Jongyong Park

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Jongmin Bae

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Shinhyun Choi

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor’s non-reliability issues have been one of the main obstacles for the development of memristor-based artificial neurons and neuromorphic computings. Here, we show a memristor 1R cross-bar array without transistor devices for individual memristor access with low variation, 100% yield, large dynamic range, and fast speed for artificial neuron and neuromorphic computing. Based on the developed memristor, we experimentally demonstrate a memristor-based neuron with leaky-integrate and fire property with excellent reliability. Furthermore, we develop a neuro-memristive computing system based on the short-term memory effect of the developed memristor for efficient processing of sequential data. Our neuro-memristive computing system successfully trains and generates bio-medical sequential data (antimicrobial peptides) while using a small number of training parameters. Our results open up the possibility of memristor-based artificial neurons and neuromorphic computing systems, which are essential for energy-efficient edge computing devices.

Suggested Citation

  • See-On Park & Hakcheon Jeong & Jongyong Park & Jongmin Bae & Shinhyun Choi, 2022. "Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30539-6
    DOI: 10.1038/s41467-022-30539-6
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    References listed on IDEAS

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    Cited by:

    1. Li, Xing & Zou, Jianxun & Feng, Zhe & Wu, Zuheng & Xu, Zuyu & Yang, Fei & Zhu, Yunlai & Dai, Yuehua, 2023. "Thermal design engineering for improving the variation of memristor threshold," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    2. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Sanghyeon Choi & Jaeho Shin & Gwanyeong Park & Jung Sun Eo & Jingon Jang & J. Joshua Yang & Gunuk Wang, 2024. "3D-integrated multilayered physical reservoir array for learning and forecasting time-series information," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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