Copper oxide memristor as artificial synapses emulating Hebbian symmetric and asymmetric learning behavior for neuromorphic computing beyond von Neumann architecture

Abstract

Beyond von Neumann’s architecture, artificial neural network-based neuromorphic computing in a simple two-terminal resistive switching device is considered the future potential technology for simultaneous data processing and storage. These are also compatible with low-power consumption nanoelectronic devices and, thus, suitable for applications such as image recognition toward solving complex pattern recognition problems. Herein, motivated by the human biological brain, we successfully synthesized low-cost RRAM devices using the thermal oxidation of Cu, i.e., CuO as the active material together with Cu as the top electrode and FTO as the bottom contact for a two-terminal resistive switching device, and investigated characteristics for neuromorphic computing. Cu/CuO/FTO-based devices showed excellent bipolar analog RRAM characteristics with 150 repeatable cycles, retention for 11 000 s, and DC pulse endurance for 5000 cycles. Moreover, devices exhibit a remarkable mimicking ability, demonstrating spike time-dependent plasticity (STDP), pulse-paired facilitation (PPF), synaptic weight, and learning and forgetting characteristics, substantiating the recognition ability. Furthermore, the artificial neural network synaptic membrane exhibits excellent long-term (LTP) and short-term (STP) potentiation for six consecutive cycles. Thus, the present work on Cu/CuO/FTO-based devices provides a detailed understanding of CuO active material-based resistive switching with a potential for neuromorphic computing beyond the von Neumann architecture.