Memristive Devices Based on Necklace-like Structure Ag@TiO₂ Nanowire Networks for Neuromorphic Learning and Reservoir Computing

Abstract

Neuromorphic nanowire networks are of broad interest for applications in burgeoning memristive devices and neuromorphic computing areas due to their interesting features such as neural-like topology and nonlinear dynamics. However, the complexity of the neuromorphic nanowire network's behavior and in materia reservoir computing with imperfect device performance still hampers a straight transfer into emerging computing applications. Herein, reliable memristive devices based on unique necklace-like structure Ag@TiO 2 nanowire networks are demonstrated for neuromorphic learning and reservoir computing. The memristive devices utilizing necklace-like structure Ag@TiO 2 nanowire networks exhibit stable volatile threshold switching characteristics, with a ratio of up to 10 5 , low threshold voltage (<1 V), good endurance, and high uniformity. Besides, the devices have been successfully used to emulate diverse functions of synapses by exploiting the Ag filament dynamics within the nanowire network, including short-term plasticity, and transition from short-term plasticity to long-term plasticity. The nanowire networks that offer nonlinear and short-term dynamics are further harnessed to build a reservoir computing system for the waveform classification task, manifesting its great potential for the development of nextgeneration reservoir hardware.