期刊
ADVANCED MATERIALS
卷 33, 期 46, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202006469
关键词
artificial synapses; memristive materials; neurons; synaptic plasticity
类别
资金
- Ministry of Education, Singapore [MOE2017-T2-2-110]
- Agency for Science, Technology and Research (A*STAR) under its AME program [A1883c0011, A1983c0038]
- National Research Foundation, the Prime Minister's Office of Singapore under its NRF Investigatorship Programme [NRF-NRFI05-2019-0003]
- King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [OSR-2018-CRG7-3736]
- National Natural Science Foundation of China [21771135, 21871071, 21774061, 61905121]
- Natural Science Foundation of Jiangsu Province, China [BK20190734]
The performance of neural synapse networks depends on the characteristics of synaptic learning, requiring the design of reliable multi-functional neural synaptic devices to achieve intelligent functions.
Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据