Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore.
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 119077, Singapore.
Adv Mater. 2021 Nov;33(46):e2006469. doi: 10.1002/adma.202006469. Epub 2021 Apr 9.
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.
神经形态计算有望以比传统的冯·诺依曼计算架构更节能的方式构建下一代智能系统。忆阻器硬件模拟生物神经元和突触，具有高速运行和低功耗的特点，能够实现高能效和低面积的脑启发式计算。本文重点介绍了用于神经形态计算的忆阻器材料和模拟突触功能的最新进展。介绍了可以通过忆阻器设备模拟的生物神经元和突触的工作原理和特性。除了具有不同外部刺激（如电场、磁场和光场）的器件结构和操作外，还讨论了具有丰富物理机制的忆阻器材料如何实现快速、可靠和低功耗的神经形态应用。最后，检查了设备要求，并对开发用于器件工程和计算科学的忆阻器材料的挑战进行了展望。
