Threshold Switching Memristor Based on 2D SnSe for Nociceptive and Leaky-Integrate and Fire Neuron Simulation
Yuwei Qin, Mengfan Wu, Niannian Yu, Ziqi Chen, Junhui Yuan, Jiafu Wang
Abstract
Multifunctional neuromorphic devices to tackle complex tasks are highly desirable for the development of artificial neural networks. Threshold switching (TS) memory, which exhibits volatile abrupt resistance change under external electric fields, is capable of emulating multiple biological behaviors because of its rich temporal dynamics. Here, a TS device based on two-dimensional (2D) SnSe is demonstrated. Owing to the diffusive dynamics of Ag ions in SnSe, intrinsic stochasticity of the TS behavior is observed, which can be exploited to construct a compact stochastic Leaky-Integrate and Fire (LIF) model with improved performance in spiking neuron network (SNN). Moreover, an artificial nociceptor is constructed based on the 2D TS device, successfully emulating typical nociceptive features of “threshold”, “relaxation”, “no adaptation”, “hyperalgesia” and “allodynia”. The realization of bioinspired devices with combined sensory and information processing abilities paves the way for developing neuromorphic electronics for SNN and humanoid robots.