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<p>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 (ZnPS<sub>3</sub>) 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 (10<sup>7</sup>), and 256 distinct conductive states, ideal for implementing adaptive neuromorphic computing. These ZnPS<sub>3</sub> 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 ZnPS<sub>3</sub>-based adaptive neuromorphic computing for energy-efficient spatiotemporal signal processing and recognition, advancing the development of ultralow-energy brain-inspired computing systems.</p>

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Ultralow energy adaptive neuromorphic computing using reconfigurable zinc phosphorus trisulfide memristors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Yun Ji,
  • Lin Wang,
  • Yinfeng Long,
  • Jinyong Wang,
  • Haofei Zheng,
  • Zhi Gen Yu,
  • Yong-Wei Zhang,
  • Kah-Wee Ang

摘要

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.