A single synaptic device with inherent learning and memory functions is demonstrated based on an amorphous InGaZnO (α‐IGZO) memristor; several essential synaptic functions are simultaneously achieved in such a single device, including nonlinear transmission characteristics, spike‐rate‐dependent and spike‐timing‐dependent plasticity, long‐term/short‐term plasticity (LSP and STP) and “learning‐experience” behavior. These characteristics bear striking resemblances to certain learning and memory functions of biological systems. Especially, a “learning‐experience” function is obtained for the first time, which is thought to be related to the metastable local structures in α‐IGZO. These functions are interrelated: frequent stimulation can cause an enhancement of LTP, both spike‐rate‐dependent and spike‐timing‐dependent plasticity is the same on this point; and, the STP‐to‐LTP transition can occur through repeated “stimulation” training. The physical mechanism of device operation, which does not strictly follow the memristor model, is attributed to oxygen ion migration/diffusion. A correlation between short‐term memory and ion diffusion is established by studying the temperature dependence of the relaxation processes of STP and ion diffusion. The realization of important synaptic functions and the establishment of a dynamic model would promote more accurate modeling of the synapse for artificial neural network.

By introducing Ag nanoclusters (NCs), ZnO-based resistive switching memory devices offer improved performance, including improved uniformity of switching parameters, and increased switching speed with excellent reliability. These Ag NCs are formed between the top-electrode (cathode) and the switching layer by an electromigration process in the initial several switching cycles. The electric field can be enhanced around Ag NCs due to their high surface curvature. The enhanced local-electric-field (LEF) results in (1) the localization of the switching site near Ag NCs, where oxygen-vacancy-based conducting filaments have a simple structure, and tend to connect Ag NCs along the LEF direction; (2) an increase in migration and recombination rates of oxygen ions and oxygen vacancies. These factors are responsible for the improvement in device performance.

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Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.

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