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- Title
Artificial Neurons Based on Ag/V 2 C/W Threshold Switching Memristors.
- Authors
Wang, Yu; Chen, Xintong; Shen, Daqi; Zhang, Miaocheng; Chen, Xi; Chen, Xingyu; Shao, Weijing; Gu, Hong; Xu, Jianguang; Hu, Ertao; Wang, Lei; Xu, Rongqing; Tong, Yi
- Abstract
Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.
- Subjects
ARTIFICIAL neural networks; MEMRISTORS; NEURONS
- Publication
Nanomaterials (2079-4991), 2021, Vol 11, Issue 11, p2860
- ISSN
2079-4991
- Publication type
Article
- DOI
10.3390/nano11112860