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Ultralow energy adaptive neuromorphic computing using reconfigurable zinc phosphorus trisulfide memristors

Author

Listed:
  • Yun Ji

    (National University of Singapore)

  • Lin Wang

    (Shanghai Jiao Tong University)

  • Yinfeng Long

    (Shanghai Jiao Tong University)

  • Jinyong Wang

    (National University of Singapore)

  • Haofei Zheng

    (National University of Singapore)

  • Zhi Gen Yu

    (Technology and Research (A*STAR))

  • Yong-Wei Zhang

    (Technology and Research (A*STAR))

  • Kah-Wee Ang

    (National University of Singapore)

Abstract

Reconfigurable devices enable adaptive neuromorphic computing by dynamically allocating circuit resources. However, integrating diverse functionalities with ultralow energy consumption in a single device remains challenging. Here, we demonstrate reconfigurable zinc phosphorus trisulfide (ZnPS3) memristors that exhibit both volatile and non-volatile switching with superior performance metrics, including a low switching voltage (~0.180 V), minimal energy consumption (143 aJ per volatile switching), high on/off ratio (107), and 256 distinct conductive states, ideal for implementing adaptive neuromorphic computing. These ZnPS3 memristors can be reconfigured using a single electrical pulse, allowing for on-demand emulation of neuron-like temporal dynamics and synapse-like weight memorization. Leveraging these device characteristics, we developed a reservoir computing network that integrates dynamic physical reservoirs with steady-weighted readouts, successfully achieving 99% accuracy in electrocardiogram classification. Our findings highlight the potential of ZnPS3-based adaptive neuromorphic computing for energy-efficient spatiotemporal signal processing and recognition, advancing the development of ultralow-energy brain-inspired computing systems.

Suggested Citation

  • Yun Ji & Lin Wang & Yinfeng Long & Jinyong Wang & Haofei Zheng & Zhi Gen Yu & Yong-Wei Zhang & Kah-Wee Ang, 2025. "Ultralow energy adaptive neuromorphic computing using reconfigurable zinc phosphorus trisulfide memristors," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62306-8
    DOI: 10.1038/s41467-025-62306-8
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