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Mimicking the cognitive functions of the brain in hardware is a primary challenge for several fields, including device physics, neuromorphic engineering, and biological neuroscience. A key element in cognitive hardware systems is the ability to learn via biorealistic plasticity rules, combined with the area scaling capability to enable integration of high-density neuron/synapse networks. To this purpose, resistive switching memory (RRAM) devices have recently attracted a strong interest as potential synaptic elements. Here, we present a novel hybrid 4-transistors/1-resistor synapse capable of spike-rate-dependent plasticity. The frequency-dependent learning behavior of the synapse is shown by experiments on HfO2 RRAM devices. Unsupervised learning, update, and recognition of one or more visual patterns in sequence is demonstrated at the level of neural network, thus, supporting the feasibility of hybrid CMOS/RRAM integrated circuits matching the learning capability in the human brain.

A 4-Transistors/1-Resistor Hybrid Synapse Based on Resistive Switching Memory (RRAM) Capable of Spike-Rate-Dependent Plasticity (SRDP)