Journal
NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-35160-1
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Funding
- National Key Research and Development Program of China [2021YFA1202600]
- NSFC [92064009, 22175042]
- Shanghai Rising-Star Program [19QA1400800]
- National Postdoctoral Program for Innovative Talents [BX2021070]
- China Postdoctoral Science Foundation [2022TQ0068, 2021M700026]
- Science and Technology Commission of Shanghai Municipality [22501100900]
- Zhejiang Lab's International Talent Fund for Young Professionals
- Young Scientist Project of MOE Innovation Platform
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The study reports a reconfigurable and ultra-low power textile memristor network, which has the ability to integrate discharge function and reduce the complexity of neuron circuits. A smart textile system for warm fabric application is successfully constructed.
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. Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles. Here, authors report an ultralow-power textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics and firing energy consumption of 1.9 fJ/spike.
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