Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO₂ Memristor for Plausible Neuromorphic Systems

Abstract

In this article, we introduce a new class of p-type transparent conductive oxide (TCO) CuCrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (150 nm) heterogeneously integrated onto fluorine doped tin oxide (FTO)/glass for forming-free memristor-based neuromorphic applications. The fabricated Al/CuCrO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /FTO memristors demonstrate a reliable bipolar resistive switching with an ON/ OFF ratio of 1000. The retention of the device was found to be steady even beyond 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> s, which demonstrates its nonvolatility. The current-voltage (I -V ) characteristics were fit to evaluate its transport properties and a band diagram was projected to have a better insight of the device operational principles. To validate the experimental observations, a new model has been developed, and the simulated I -V behavior was analogous to the experimental one. Efforts were then devoted to observe long-term potentiation (LTP) and long-term depression (LTD) utilizing identical but opposite pulses to evaluate the device's efficacy for synaptic applications. The synaptic behavior was well controlled by the pulse (pulse amplitude and width) variations. The conductance change was found to be symmetric and then saturated, which reflects the popular biological Hebbian rules. Finally, a long-term synaptic modulation has been implemented by establishing the spike rate dependent plasticity (SRDP) rule, which is a part of spiking neural networks and advantageous to mimic the brain's capability at low power. All the obtained experimental results were systematically corroborated by neural network simulation. Overall, our approach provides a new road map toward the development of TCO-based alternative memristors, which can be employed to mimic the synaptic plasticity for energy-efficient bioinspired neuromorphic systems and non-von Neumann computer architectures.