Refreshable memristor via dynamic allocation of ferro-ionic phase for neural reuse

Jiangang Chen, Zhixing Wen, Fan Yang, Renji Bian, Qirui Zhang, Er Pan, Yuelei Zeng, Xiao Luo, Qing Liu, Liang-Jian Deng and Fucai Liu ()
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Jiangang Chen: University of Electronic Science and Technology of China
Zhixing Wen: University of Electronic Science and Technology of China
Fan Yang: University of Electronic Science and Technology of China
Renji Bian: University of Electronic Science and Technology of China
Qirui Zhang: University of Electronic Science and Technology of China
Er Pan: University of Electronic Science and Technology of China
Yuelei Zeng: University of Electronic Science and Technology of China
Xiao Luo: University of Electronic Science and Technology of China
Qing Liu: University of Electronic Science and Technology of China
Liang-Jian Deng: University of Electronic Science and Technology of China
Fucai Liu: University of Electronic Science and Technology of China

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Neural reuse can drive organisms to generalize knowledge across various tasks during learning. However, existing devices mostly focus on architectures rather than network functions, lacking the mimic capabilities of neural reuse. Here, we demonstrate a rational device designed based on ferroionic CuInP2S6, to accomplish the neural reuse function, enabled by dynamic allocation of the ferro-ionic phase. It allows for dynamic refresh and collaborative work between volatile and non-volatile modes to support the entire neural reuse process. Notably, ferroelectric polarization can remain consistent even after undergoing the refresh process, providing a foundation for the shared functionality across multiple tasks. By implementing neural reuse, the classification accuracy of neuromorphic hardware can improve by 17%, while the consumption is reduced by 40%; in multi-task scenarios, its training speed is accelerated by 2200%, while its generalization ability is enhanced by 21%. Our results are promising towards building refreshable hardware platforms based on ferroelectric-ionic combination capable of accommodating more efficient algorithms and architectures.

Date: 2025
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DOI: 10.1038/s41467-024-55701-0

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