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Ultrathin SrTiO_(3)-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing 被引量:1
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作者 Fang Nie Jie Wang +9 位作者 Hong Fang Shuanger Ma Feiyang Wu Wenbo Zhao shizhan wei Yuling Wang Le Zhao Shishen Yan Chen Ge Limei Zheng 《Materials Futures》 2023年第3期156-163,共8页
Artificial synapses are electronic devices that simulate important functions of biological synapses,and therefore are the basic components of artificial neural morphological networks for brain-like computing.One of th... Artificial synapses are electronic devices that simulate important functions of biological synapses,and therefore are the basic components of artificial neural morphological networks for brain-like computing.One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible,especially their self-adaptive ability to external stimuli.Here,we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO_(3)/Nb:SrTiO_(3)heterojunction type memristor.Diverse functions of synaptic learning,such as short-term/long-term plasticity(STP/LTP),transition from STP to LTP,learning–forgetting–relearning behaviors,associative learning and dynamic filtering,are all bio-realistically implemented in a single device.The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion,which give rise to the coexistence of volatile-and nonvolatile-type resistive switching.This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure,showing great application potential for a compact and low-power neuromorphic computing system. 展开更多
关键词 MEMRISTOR artificial synapse synaptic plasticity associative learning learning-experience
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