基于Ge-Ga-Sb介质的全相变脉冲神经网络的设计

Design of all-phase-change-memory spiking neural network enabled by Ge-Ga-Sb compound

人工脉冲神经网络通常由多个异质结构的电路单元构成,其中包括具备积分点火功能来产生脉冲信号的神经元模拟器,以及具备记忆功能的突触器件.在本文中,我们设计了一种能进行“同质集成”的相变存储介质Ge-Ga-Sb(GGS)器件,该器件能够同时实现神经元和突触的模拟.在先前的研究中,GGS材料表现出优秀的数据存储功能,例如它具备较高的工作温度(281℃)、较高的十年数据保存温度(230℃)以及较低的电阻漂移.当对该器件改用短脉冲电学操作时,GGS器件首先会发生几个数量级的电阻突变,然后紧接着发生连续的电阻降低.通过透射电子显微镜发现,电阻突变是因为电极之间产生了结晶的导电通道,而电阻缓变是因为导电通道的变粗以及在通道内产生材料分相所致.这种“突变-缓变”的电阻变化特性既可以用来模拟神经元的积分点火功能,也可以模拟突触权重的变化.基于此器件设计的全相变脉冲神经网络,可以实现高达90%的手写数字识别率.

The implementation of artificial spiking neural network(SNN)usually takes advantage of multiple heterogeneous circuits to mimic either neurons which generate spiking pulses,or synapses which store the weights of event correlations.Here,we design a homogeneous device using GeGa-Sb(GGS)as a phase-change-memory(PCM)material which can do both jobs.The GGS compound shows high stability when used in data storage,such as high working temperature(281℃)and high 10-years data retention temperature(230℃),as well as low resistance drift.Interestingly,when the as-fabricated GGS device is set by iterative narrow-width electric pulses,it first experiences an abrupt resistance drop by two orders of magnitude,followed by a continuous resistance decrease.This unique abrupt-to-progressive transition can be used to mimic both neuronal and synaptic functions,mechanistically enabled by the formation of conductive channels and the continuous growth with the phase separation of crystalline areas.To this end,we propose an all-PCM SNN,which is emulated to have high accuracy(90%)in the standard pattern recognition.