Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing,Advanced Materials

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Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing
Advanced Materials ( IF 27.4 ) Pub Date : 2021-04-09 , DOI: 10.1002/adma.202006469
Hongyu Bian ₁ , Yi Yiing Goh _{1,

2} , Yuxia Liu _{1,

3} , Haifeng Ling ₄ , Linghai Xie ₄ , Xiaogang Liu _{1,

3}

Affiliation

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.

中文翻译：

用于人工突触和神经形态计算的刺激响应忆阻材料

神经形态计算有望以比传统冯诺依曼计算架构更节能的方式构建下一代智能系统。模仿生物神经元和突触的忆阻硬件提供高速操作和低功耗，从而实现能源和面积效率高的类脑计算。在这里，重点介绍了模拟神经形态计算的突触功能的忆阻材料和策略的最新进展。介绍了可被忆阻器模拟的生物神经元和突触的工作原理和特点。除了具有不同外部刺激（如电场、磁场和光场）的器件结构和操作之外，具有多种潜在物理机制的忆阻材料如何能够实现快速、可靠、还讨论了低功耗神经形态应用。最后，检查了设备要求，并给出了为设备工程和计算科学开发忆阻材料所面临的挑战。

更新日期：2021-04-09

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