基于Ge-Ga-Sb介质的全相变脉冲神经网络的设计

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 Ge-Ga-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°C) and high 10-years data retention temperature (230°C), 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.