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.
In
order to imitate brain-inspired biological information processing
systems, various neuromorphic computing devices have been proposed,
most of which were prepared on rigid substrates and have energy consumption
levels several orders of magnitude higher than those of biological
synapses (∼10 fJ per spike). Herein, a new type of wearable
organic ferroelectric artificial synapse is proposed, which has two
modulation modes (optical and electrical modulation). Because of the
high photosensitivity of organic semiconductors and the ultrafast
polarization switching of ferroelectric materials, the synaptic device
has an ultrafast operation speed of 30 ns and an ultralow power consumption
of 0.0675 aJ per synaptic event. Under combined photoelectric modulation,
the artificial synapse realizes associative learning. The proposed
artificial synapse with ultralow power consumption demonstrates good
synaptic plasticity under different bending strains. This provides
new avenues for the construction of ultralow power artificial intelligence
system and the development of future wearable devices.
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