Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic   Computing

doi:10.1109/TED.2016.2643162

CORC > 北京大学 > 信息科学技术学院

	Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic Computing
	Huang, Peng ; Zhu, Dongbin ; Chen, Sijie ; Zhou, Zheng ; Chen, Zhe ; Gao, Bin ; Liu, Lifeng ; Liu, Xiaoyan ; Kang, Jinfeng
刊名	IEEE TRANSACTIONS ON ELECTRON DEVICES
	2017
关键词	Electronic synapse neuromorphic computing pattern classification stochastic learning RESISTIVE-SWITCHING MEMORY METAL-OXIDE RRAM SYSTEMS SYNAPSES NETWORK COMPUTATION
DOI	10.1109/TED.2016.2643162
英文摘要	HfOx-based resistive switching device has been explored as one of the promising candidates for the electronic synapses of neuromorphic computing systems due to its high performance, low cost, and compatibility withCMOS technology. Tomeet the codesign requirement of HfOx-based electronic synapses with CMOS neurons in the neuromorphic computing systems, a compact model that can capture the synaptic futures of HfOx-based resistive switching device is developed. The developed model can accurately describe the multilevel conductance transition behaviors during RESET process for depression learning as well as the binary stochastic transition behavior during SET process for potentiation learning. After the verifica-tion with experimental data, the model is used to simulate a winner-take-all neural network to classify patterns with unsupervised competitive learning algorithm. Simulation results imply that the average recognition accuracy would decrease with the increase of the resistance variation of low resistance state (LRS) due to the "trap"effect. Guided by the simulation, a synapse cell consisted of a HfOX-based device and a fixed resistor series connection is proposed to achieve almost 100% recognition accuracy even if the resistance variation of LRS is 50%.; National Natural Science Foundation of China [61334007, 61421005, 61404006]; SCI(E); ARTICLE; 2; 614-621; 64
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/475425]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Huang, Peng,Zhu, Dongbin,Chen, Sijie,et al. Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic Computing[J]. IEEE TRANSACTIONS ON ELECTRON DEVICES,2017.
APA	Huang, Peng.,Zhu, Dongbin.,Chen, Sijie.,Zhou, Zheng.,Chen, Zhe.,...&Kang, Jinfeng.(2017).Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic Computing.IEEE TRANSACTIONS ON ELECTRON DEVICES.
MLA	Huang, Peng,et al."Compact Model of HfOX-Based Electronic Synaptic Devices for Neuromorphic Computing".IEEE TRANSACTIONS ON ELECTRON DEVICES (2017).