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用于可再生同质神经形态计算系统的具有稳定同步突触和神经元功能的可重构银/氧化铪/氧化镍/铂忆阻器

Reconfigurable Ag/HfO/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System.

作者信息

Chen Jiaqi, Liu Xingqiang, Liu Chang, Tang Lin, Bu Tong, Jiang Bei, Qing Yahui, Xie Yulu, Wang Yong, Shan Yongtao, Li Ruxin, Ye Cong, Liao Lei

机构信息

Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China.

Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China.

出版信息

Nano Lett. 2024 May 1;24(17):5371-5378. doi: 10.1021/acs.nanolett.4c01319. Epub 2024 Apr 22.

Abstract

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

摘要

人工突触和仿生神经元在高效计算范式中具有巨大潜力。然而,神经形态计算中对特定电子器件的复杂要求使得忆阻器面临工艺简化和通用性的挑战。在此,通过合规电流控制的银细丝设计了可重构的Ag/HfO/NiO/Pt忆阻器,以实现挥发性和非挥发性模式之间的可行切换,从而实现稳定且可重构的突触和神经元功能。一种神经形态计算系统有效地复制了生物突触权重变化,并持续完成人工神经元的激发和重置,该系统由基于同型Ag/HfO/NiO/Pt忆阻器的仿生突触和人工神经元组成。Ag/HfO/NiO/Pt忆阻器的这种可重构电性能利用了简化的硬件设计,并提供了高密度的集成电路,这对未来的神经网络具有巨大潜力。

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