Biological Spiking Synapse Constructed from Solution Processed Bimetal Core–Shell Nanoparticle Based Composites. Issue 28 (27th May 2018) - Zhou, Li; Mao, Jing‐Yu; Ren, Yi; Yang, Jia‐Qin; Zhang, Shi‐Rui; Zhou, Ye; Liao, Qiufan; Zeng, Yu‐Jia; Shan, Haiquan; Xu, Zongxiang; Fu, Jingjing; Wang, Yan; et al.
Abstract: Inspired by the highly parallel processing power and low energy consumption of the biological nervous system, the development of a neuromorphic computing paradigm to mimic brain‐like behaviors with electronic components based artificial synapses may play key roles to eliminate the von Neumann bottleneck. Random resistive access memory (RRAM) is suitable for artificial synapse due to its tunable bidirectional switching behavior. In this work, a biological spiking synapse is developed with solution processed Au@Ag core–shell nanoparticle (NP)‐based RRAM. The device shows highly controllable bistable resistive switching behavior due to the favorable Ag ions migration and filament formation in the composite film, and the good charge trapping and transport property of Au@Ag NPs. Moreover, comprehensive synaptic functions of biosynapse including paired‐pulse depression, paired‐pulse facilitation, post‐tetanic potentiation, spike‐time‐dependent plasticity, and the transformation from short‐term plasticity to long‐term plasticity are emulated. This work demonstrates that the solution processed bimetal core–shell nanoparticle‐based biological spiking synapse provides great potential for the further creation of a neuromorphic computing system. Abstract : A biological spiking synapse constructed from solution processed bimetal core–shell nanoparticle based composites is reported. The device can emulate important synaptic behaviors including spike‐rate‐dependent plasticity, spike‐time‐dependent plasticity, and transformation from short‐term plasticity to long‐term plasticity. This work demonstrates that the polymer–metal nanoparticles composites based artificial synapse have great potential for the realization of next‐generation neuromorphic computing system.