面向神经形态感知和计算的柔性忆阻器基脉冲神经元

Flexible memristive spiking neuron for neuromorphic sensing and computing

受人脑工作模式的启发,脉冲神经元作为人工感知系统和神经形态计算体系的基本计算单元发挥着重要作用.然而,基于传统互补金属氧化物半导体技术的神经元电路结构复杂,功耗高,且缺乏柔韧性,不利于大规模集成和与人体兼容的柔性感知系统的应用.本文制备的柔性忆阻器展示出了稳定的阈值转变特性和优异的机械弯折特性,其弯折半径可达1.5 mm,弯折次数可达10^(4)次.基于此器件构建的神经元电路实现了神经元的关键积分放电特性,且其频率-输入电压关系具有整流线性单元相似性,可实现基于转换法的脉冲神经网络中神经元的非线性处理功能.此外,基于电子传输机制和构建的核壳模型,对柔性忆阻器的工作机制进行分析,提出了电场和热激发主导的阈值转变机制;进一步对忆阻器和神经元的电学特性进行电路仿真模拟,验证了柔性忆阻器和神经元电路工作机制的合理性.本文对柔性神经元的研究可为神经形态感知和计算系统的构建提供硬件基础和理论指导.

Inspired by the working modes of the human brain,the spiking neuron plays an important role as the basic computing unit of artificial perception systems and neuromorphic computing systems.However,the neuron circuit based on complementary metal-oxide-semiconductor technology has a complex structure,high power consumption,and limited flexibility.These features are not conducive to the large-scale integration and the application of flexible sensing systems compatible with the human body.The flexible memristor prepared in this work shows stable threshold switching characteristics and excellent mechanical bending characteristics with bending radius up to 1.5 mm and bending times up to 104.The compact neuron circuit based on this device shows the key features of the neuron,such as threshold-driven spiking,all-or-nothing,refractory period,and strength-modulated frequency response.The frequency-input voltage relationship of the neuron shows the similarity of the rectified linear unit,which can be used to simulate the function of rectified linear unit in spiking neural networks.In addition,based on the electron transport mechanism,a core-shell model is introduced to analyze the working mechanism of the flexible memristor and explain the output characteristics of the neuron.In this model,the shell region consisting of Nb_(2)O_(5-x) is subjected to ohmic conduction,while the core region consisting of NbO_(2) is dominated by Poole-Frenkel conduction.These two mechanisms,combined with Newton’s law of cooling,dominate the threshold switching behavior of flexible memristor device.Furthermore,the threshold switching characteristic of the memristor is simulated,verifying the rationality of the working mechanism of the flexible memristor.Considering the fact that the threshold voltage decreases with temperature increasing,a correction term is added to the temperature of the shell region.Subsequently,the output characteristics of the neuron regulated by the input voltage are simulated.The simulation results show that the frequency increases but the threshold voltage decreases with the input voltage increasing,which is consistent with the experimental result.The introduction of the correction term confirms the influence of the thermal accumulation effect of the flexible substrate on neuron output characteristics.Finally,we build a spiking neural network based on memristive spiking neurons to implement handwriting recognition,achieving a 95.6% recognition rate,which is comparable to the ideal result of the artificial neural network(96%).This result shows the potential application of the memristive spiking neurons in neuromorphic computing.In this paper,the study of flexible neurons can guide the design of neuromorphic sensing and computing systems.