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An artificial synapse based on molecular junctions

Author

Listed:
  • Yuchun Zhang

    (National Center for Nanoscience and Technology)

  • Lin Liu

    (National Center for Nanoscience and Technology
    University of Chinese Academy of Sciences)

  • Bin Tu

    (National Center for Nanoscience and Technology)

  • Bin Cui

    (School of Physics, Shandong University)

  • Jiahui Guo

    (National Center for Nanoscience and Technology
    University of Chinese Academy of Sciences)

  • Xing Zhao

    (National Center for Nanoscience and Technology)

  • Jingyu Wang

    (National Center for Nanoscience and Technology
    University of Chinese Academy of Sciences)

  • Yong Yan

    (National Center for Nanoscience and Technology
    University of Chinese Academy of Sciences
    University of Science and Technology Beijing)

Abstract

Shrinking the size of the electronic synapse to molecular length-scale, for example, an artificial synapse directly fabricated by using individual or monolayer molecules, is important for maximizing the integration density, reducing the energy consumption, and enabling functionalities not easily achieved by other synaptic materials. Here, we show that the conductance of the self-assembled peptide molecule monolayer could be dynamically modulated by placing electrical biases, enabling us to implement basic synaptic functions. Both short-term plasticity (e.g., paired-pulse facilitation) and long-term plasticity (e.g., spike-timing-dependent plasticity) are demonstrated in a single molecular synapse. The dynamic current response is due to a combination of both chemical gating and coordination effects between Ag+ and hosting groups within peptides which adjusts the electron hopping rate through the molecular junction. In the end, based on the nonlinearity and short-term synaptic characteristics, the molecular synapses are utilized as reservoirs for waveform recognition with 100% accuracy at a small mask length.

Suggested Citation

  • Yuchun Zhang & Lin Liu & Bin Tu & Bin Cui & Jiahui Guo & Xing Zhao & Jingyu Wang & Yong Yan, 2023. "An artificial synapse based on molecular junctions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-35817-5
    DOI: 10.1038/s41467-023-35817-5
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    References listed on IDEAS

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    1. Xiaoping Chen & Bernhard Kretz & Francis Adoah & Cameron Nickle & Xiao Chi & Xiaojiang Yu & Enrique Barco & Damien Thompson & David A. Egger & Christian A. Nijhuis, 2021. "A single atom change turns insulating saturated wires into molecular conductors," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Xinkai Qiu & Ryan C. Chiechi, 2022. "Printable logic circuits comprising self-assembled protein complexes," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Oren Ben Dor & Shira Yochelis & Anna Radko & Kiran Vankayala & Eyal Capua & Amir Capua & See-Hun Yang & Lech Tomasz Baczewski & Stuart Stephen Papworth Parkin & Ron Naaman & Yossi Paltiel, 2017. "Magnetization switching in ferromagnets by adsorbed chiral molecules without current or external magnetic field," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    4. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    5. Yanan Zhong & Jianshi Tang & Xinyi Li & Bin Gao & He Qian & Huaqiang Wu, 2021. "Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    6. Xiaojian Zhu & Qiwen Wang & Wei D. Lu, 2020. "Memristor networks for real-time neural activity analysis," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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