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

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.

Automated summary

Researchers successfully synthesized low-cost Cu/CuO/FTO-based devices and investigated their characteristics for neuromorphic computing. These devices exhibited excellent bipolar analog RRAM characteristics and demonstrated remarkable mimicking ability similar to the human brain, substantiating their recognition ability. This study provides a detailed understanding of CuO active material-based resistive switching and its potential for neuromorphic computing beyond the von Neumann architecture.