Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

Tianyu Wang, Jialin Meng, Xufeng Zhou, Yue Liu, Zhenyu He, Qi Han, Qingxuan Li, Jiajie Yu, Zhenhai Li, Yongkai Liu, Hao Zhu, Qingqing Sun, David Wei Zhang, Peining Chen (), Huisheng Peng and Lin Chen ()
Additional contact information
Tianyu Wang: Fudan University
Jialin Meng: Fudan University
Xufeng Zhou: Fudan University
Yue Liu: Fudan University
Zhenyu He: Fudan University
Qi Han: Fudan University
Qingxuan Li: Fudan University
Jiajie Yu: Fudan University
Zhenhai Li: Fudan University
Yongkai Liu: Fudan University
Hao Zhu: Fudan University
Qingqing Sun: Fudan University
David Wei Zhang: Fudan University
Peining Chen: Fudan University
Huisheng Peng: Fudan University
Lin Chen: Fudan University

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.

Date: 2022
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DOI: 10.1038/s41467-022-35160-1

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