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Recent Progress in Artificial Synapses Based on Two-Dimensional van der Waals Materials for Brain-Inspired Computing
ACS Applied Electronic Materials ( IF 4.7 ) Pub Date : 2020-01-07 , DOI: 10.1021/acsaelm.9b00694
Seunghwan Seo 1 , Je-Jun Lee 1 , Ho-Jun Lee 1 , Hae Won Lee 1 , Seyong Oh 1 , Je Jun Lee 2 , Keun Heo 1 , Jin-Hong Park 1, 3
Affiliation  

On the basis of recent research, brain-inspired parallel computing is considered as one of the most promising technologies for efficiently handling large amounts of informational data. In general, this type of parallel computing is called neuromorphic computing; it operates on the basis of hardware-neural-network (HW-NN) platforms consisting of numerous artificial synapses and neurons. Extensive research has been conducted to implement artificial synapses with characteristics required to ensure high-level performance of HW-NNs in terms of device density, energy efficiency, and learnings accuracy. Recently, artificial synapses—specifically, diode- and transistor-type synapses—based on various two-dimensional (2D) van der Waals (vdW) materials have been developed. Unique properties of such 2D vdW materials allow for notable improvements in synaptic performances in terms of learning capability, scalability, and power efficiency, thereby highlighting the feasibility of the 2D vdW synapses in improving the performance of HW-NNs. In this review, we introduce the desirable characteristics of artificial synapses required to ensure high-level performance of neural networks. Recent progress in research on artificial synapses, fabricated particularly using 2D vdW materials and heterostructures, is comprehensively discussed with respect to the weight-update mechanism, synaptic characteristics, power efficiency, and scalability.

中文翻译:

基于二维范德华材料的人工突触在脑启发计算中的最新进展

根据最近的研究,灵感来自大脑的并行计算被认为是有效处理大量信息数据的最有前途的技术之一。通常,这种类型的并行计算称为神经形态计算。它基于由大量人工突触和神经元组成的硬件-神经网络(HW-NN)平台运行。已经进行了广泛的研究来实现具有特征的人造突触,以确保HW-NN在设备密度,能效和学习准确性方面具有较高的性能。最近,已经开发了基于各种二维(2D)范德华(vdW)材料的人工突触,特别是二极管和晶体管型突触。此类2D vdW材料的独特特性可在学习能力,可扩展性和电源效率方面显着改善突触性能,从而突出了2D vdW突触在改善HW-NN性能方面的可行性。在这篇综述中,我们介绍了确保神经网络高级性能所需的人工突触的理想特性。关于权重更新机制,突触特性,功率效率和可扩展性,全面讨论了人工突触的最新研究进展,尤其是使用2D vdW材料和异质结构制造的突触。在这篇综述中,我们介绍了确保神经网络高级性能所需的人工突触的理想特性。关于权重更新机制,突触特性,功率效率和可扩展性,全面讨论了人工突触的最新研究进展,尤其是使用2D vdW材料和异质结构制造的突触。在这篇综述中,我们介绍了确保神经网络高级性能所需的人工突触的理想特性。关于权重更新机制,突触特性,功率效率和可扩展性,全面讨论了人工突触的最新研究进展,尤其是使用2D vdW材料和异质结构制造的突触。
更新日期:2020-01-07
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