Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing - Weng, Zhengjin; Ji, Tianyi; Yu, Yanling; Fang, Yong; Lei, Wei; Shafie, Suhaidi; Jindapetch, Nattha; Zhao, Zhiwei (American Chemical Society, 2024)
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@TiO2 nanowire networks are demonstrated for neuromorphic learning and reservoir computing. The memristive devices utilizing necklace-like structure Ag@TiO2 nanowire networks exhibit stable volatile threshold switching characteristics, with a ratio of up to 105, 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 next-generation reservoir hardware.