神经形态工程学
仿真
可塑性
六方氮化硼
光电子学
材料科学
记忆电阻器
计算机科学
纳米技术
电气工程
人工神经网络
工程类
机器学习
复合材料
经济
经济增长
石墨烯
作者
Yuanyuan Shi,Xianhu Liang,Bin Yuan,Victoria Chen,Haitong Li,Fei Hui,Zhouchangwan Yu,Fang Yuan,Eric Pop,H.‐S. Philip Wong,Mario Lanza
标识
DOI:10.1038/s41928-018-0118-9
摘要
Neuromorphic computing systems, which use electronic synapses and neurons, could overcome the energy and throughput limitations of today’s computing architectures. However, electronic devices that can accurately emulate the short- and long-term plasticity learning rules of biological synapses remain limited. Here, we show that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses. The devices can operate in a volatile or non-volatile regime, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity. The behaviour results from a resistive switching mechanism in the h-BN stack, based on the generation of boron vacancies that can be filled by metallic ions from the adjacent electrodes. The power consumption in standby and per transition can reach as low as 0.1 fW and 600 pW, respectively, and with switching times reaching less than 10 ns, demonstrating their potential for use in energy-efficient brain-like computing. Vertically structured electronic synapses, which exhibit both short- and long-term plasticity, can be created using layered two-dimensional hexagonal boron nitride.
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