Low Power Stochastic Neurons From SiO2-Based Bilayer Conductive Bridge Memristors for Probabilistic Spiking Neural Network Applications—Part I: Experimental Characterization

Abstract

The development of low-power neurons with an intrinsic degree of stochasticity is considered quite important for the emulation of the respective probabilistic procedures that take place within the biological neural networks. While the majority of the reported artificial neuronal configurations relies on the demonstration of well-defined spiking patterns or the realization of complicated neuromorphic properties, there is an urgent need to devise biological-like neurons with arbitrary firing capabilities. Along these lines, we present a novel threshold switching memristor from SiO₂-based bilayer conductive-bridge resistive switching memory (CBRAM) and Pt nanoparticles (NPs) used as a bottom electrode (BE) in order to implement robust stochastic neuron activity accompanied with steep transition slope [&#x007E;0.75 mV/dec(A)], large OFF-state (&#x007E;<inline-formula> <tex-math notation="LaTeX">$10^{11} \Omega $ </tex-math></inline-formula>), low switching voltage (&#x007E;200 mV), and ultrafast turn-on speed (&#x007E;50 ns). Moreover, probabilistic leaky-integrate-and-fire (LIF) neuron properties were attained by employing a simple <i>RC</i> circuit as well as tunable integrate-and-fire (IF) properties from stand-alone threshold switching elements, which are of great significance for the development of artificial spiking neuromorphic networks.