Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware

doi:10.1038/s41467-024-48631-4

IMR OpenIR

	Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware
	Liu, Long 1,2; Wang, Di 1,2; Wang, Dandan 3; Sun, Yan 4; Lin, Huai 1,2; Gong, Xiliang 4; Zhang, Yifan 1,2; Tang, Ruifeng 1,2; Mai, Zhihong 3; Hou, Zhipeng 5; Yang, Yumeng 6; Li, Peng 7; Wang, Lan 8; Luo, Qing 1,2; Li, Ling 1,2; Xing, Guozhong 1,2; Liu, Ming 1,9
通讯作者	Wang, Dandan(wangdandan@jfslab.com.cn) ; Xing, Guozhong(gzxing@ime.ac.cn) ; Liu, Ming(liuming@ime.ac.cn)
	2024-05-28
发表期刊	NATURE COMMUNICATIONS
卷号	15 期号:1 页码:12
摘要	We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures. The authors demonstrate all-spin synapses and neurons using domain wall-magnetic tunnel junctions, utilizing synergistic spin-orbit torque and Dzyaloshinskii-Moriya interaction. The intrinsic linearity is required for compact and energy-efficient bio-inspired hardware for neuromorphic computing.
资助者	National Natural Science Foundation of China (National Science Foundation of China) ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; State Key Laboratory of Integrated Chips and Systems of Fudan University ; Information Science Laboratory Center of USTC ; Qingdao Research Institute
DOI	10.1038/s41467-024-48631-4
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (National Science Foundation of China)[2021YFB3601300] ; National Key Research and Development Program of China[92365113] ; National Natural Science Foundation of China[XDB44010000] ; Strategic Priority Research Program of the Chinese Academy of Sciences ; State Key Laboratory of Integrated Chips and Systems of Fudan University ; Information Science Laboratory Center of USTC ; Qingdao Research Institute
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001234660500028
出版者	NATURE PORTFOLIO
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/186681
专题	中国科学院金属研究所
通讯作者	Wang, Dandan; Xing, Guozhong; Liu, Ming
作者单位	1.Chinese Acad Sci, Inst Microelect, Key Lab Fabricat Technol Integrated Circuits, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Hubei Jiufengshan Lab, Wuhan 430206, Hubei, Peoples R China 4.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 5.South China Normal Univ, Inst Adv Mat, Guangzhou 510006, Peoples R China 6.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 7.Univ Sci & Technol China, Sch Microelect, Hefei 230026, Peoples R China 8.Hefei Univ Technol, Sch Phys, Lab Low Dimens Magnetism & Spintron Devices, Hefei 230009, Anhui, Peoples R China 9.Fudan Univ, Frontier Inst Chip & Syst, Zhangjiang Fudan Int Innovat Ctr, State Key Lab Integrated Chips & Syst, Shanghai 200433, Peoples R China
推荐引用方式 GB/T 7714	Liu, Long,Wang, Di,Wang, Dandan,et al. Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware[J]. NATURE COMMUNICATIONS,2024,15(1):12.
APA	Liu, Long.,Wang, Di.,Wang, Dandan.,Sun, Yan.,Lin, Huai.,...&Liu, Ming.(2024).Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware.NATURE COMMUNICATIONS,15(1),12.
MLA	Liu, Long,et al."Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware".NATURE COMMUNICATIONS 15.1(2024):12.