Multitasking Memristor for High Performance and Ultralow Power Artificial Synaptic Device Application

The emergence of in-memory computing has shed light on solving high-power consumption and low computation efficiency of the traditional computers built with von Neumann architecture. Memristor, which exhibits history-dependent conductivity modulation, can simulate the synaptic behaviors in the biological brain. However, it remains as a key challenge to fabricate devices that can demonstrate a wide range of synaptic plasticity and maintain stable switching responses over repetitive operating cycles. Hereby, the memristor made of Au/Ti/TiO2/Nb:SrTiO3 (Nb:STO) heterojunction shows a partial nonvolatile bipolar resistive switching behavior with an initial high on/off switching ratio of ∼104, and “writing” and “erasing” with long endurance across 1.5 × 104 cycles. Furthermore, we experimentally developed a single device that possesses a 5-bits (32-states) reservoir computing system to recognize the binary patterns. We also demonstrated the multidata storage for neuromorphic computing in a 10 × 10 neuromorphic array to recognize the patterns of multilevel resistance states with an ultralow operation power of 4.1 pJ. In addition, various synaptic dependent plasticity performances, including spike-duration, -interval, and -number dependent plasticity, have been realized. Such an on-demand neuromorphic device exhibits a multitask shifting potential for analog bipolar memory and bistate and multistate neuromorphic networks and paves a way to develop the highly efficient memristor devices.