A Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors

Abstract Spiking encoded stochastic neural network is believed to be energy efficient and biologically plausible and an increasing effort has been made recently to translate its great cognitive power into hardware implementations. Here, a stacked indium–gallium–zinc–oxide (IGZO)‐based threshold switching memristor with essential properties as a spiking stochastic neuron is introduced. Such IGZO spiking stochastic neuron shows a sigmoid firing probability that can be tuned by the amplitude, width, and frequency of the applied pulse sequence. More importantly, the stacked configuration is experimentally demonstrated with eliminated switching variation compared to one single memristor and a narrow relative deviation (≤6.8%) of the firing probability can be achieved. The IGZO stochastic neuron is applied to perform probabilistic unsupervised learning for handwritten digit reconstruction based on a restricted Boltzmann machine and a recognition accuracy of 91.2% can be achieved. Such IGZO stochastic neuron with reproducible firing probability emulates probabilistic computing in the brain, which is of significant importance to hardware implementation of spiking neural network to analyze sensory stimuli, produce adequate motor control, and make reasonable inference.