Ultralow energy adaptive neuromorphic computing using reconfigurable zinc phosphorus trisulfide memristors

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

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-62306-8

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