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Liu T, Li X, An H, Chen S, Zhao Y, Yang S, Xu X, Zhou C, Zhang H, Zhou Y. Reconfigurable spintronic logic gate utilizing precessional magnetization switching. Sci Rep 2024; 14:14796. [PMID: 38926523 PMCID: PMC11208557 DOI: 10.1038/s41598-024-65634-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
In traditional von Neumann computing architecture, the efficiency of the system is often hindered by the data transmission bottleneck between the processor and memory. A prevalent approach to mitigate this limitation is the use of non-volatile memory for in-memory computing, with spin-orbit torque (SOT) magnetic random-access memory (MRAM) being a leading area of research. In this study, we numerically demonstrate that a precise combination of damping-like and field-like spin-orbit torques can facilitate precessional magnetization switching. This mechanism enables the binary memristivity of magnetic tunnel junctions (MTJs) through the modulation of the amplitude and width of input current pulses. Building on this foundation, we have developed a scheme for a reconfigurable spintronic logic gate capable of directly implementing Boolean functions such as AND, OR, and XOR. This work is anticipated to leverage the sub-nanosecond dynamics of SOT-MRAM cells, potentially catalyzing further experimental developments in spintronic devices for in-memory computing.
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Grants
- 12104322,12375237,52001215,12374123,11974298 National Natural Science Foundation of China
- 12104322,12375237,52001215,12374123,11974298 National Natural Science Foundation of China
- 12104322,12375237,52001215,12374123,11974298 National Natural Science Foundation of China
- 2021B1515120047,2021A1515012055 Guangdong Basic and Applied Basic Research Foundation
- 2021B1515120047,2021A1515012055 Guangdong Basic and Applied Basic Research Foundation
- ZDSYS20200811143600001 Shenzhen Science and Technology Program
- 2022YFA1603200, 2022YFA1603202 National Key R&D Program of China
- KQTD20180413181702403 Shenzhen Peacock Group Plan
- JCYJ20210324120213037 The Shenzhen Fundamental Research Fund
- National Key R&D Program of China
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Affiliation(s)
- Ting Liu
- College of Engineering Physics, and Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Shenzhen Technology University, Shenzhen, 518118, China
| | - Xiaoguang Li
- College of Engineering Physics, and Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Hongyu An
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, 518118, China
| | - Shi Chen
- College of Engineering Physics, and Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yuelei Zhao
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Sheng Yang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Xiaohong Xu
- Research Institute of Materials Science of Shanxi Normal University & Collaborative Innovation Center for Shanxi Advanced Permanent Magnetic Materials and Technology, Linfen, 041004, China
- School of Chemistry and Materials Science of Shanxi Normal University & Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education, Linfen, 041004, China
| | - Cangtao Zhou
- College of Engineering Physics, and Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Shenzhen Technology University, Shenzhen, 518118, China
| | - Hua Zhang
- College of Engineering Physics, and Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Yan Zhou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China.
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Liu L, Wang D, Wang D, Sun Y, Lin H, Gong X, Zhang Y, Tang R, Mai Z, Hou Z, Yang Y, Li P, Wang L, Luo Q, Li L, Xing G, Liu M. Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware. Nat Commun 2024; 15:4534. [PMID: 38806482 PMCID: PMC11133408 DOI: 10.1038/s41467-024-48631-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/06/2024] [Indexed: 05/30/2024] Open
Abstract
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.
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Affiliation(s)
- Long Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Di Wang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dandan Wang
- Hubei Jiufengshan Laboratory, Wuhan, Hubei, 430206, China.
| | - Yan Sun
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Huai Lin
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiliang Gong
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Yifan Zhang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ruifeng Tang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Mai
- Hubei Jiufengshan Laboratory, Wuhan, Hubei, 430206, China
| | - Zhipeng Hou
- Institute for Advanced Materials, South China Normal University, Guangzhou, 510006, China
| | - Yumeng Yang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Peng Li
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Lan Wang
- Lab of Low Dimensional Magnetism and Spintronic Devices, School of Physics, Hefei University of Technology, Hefei, 230009, Anhui, China
| | - Qing Luo
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ling Li
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guozhong Xing
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ming Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China.
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Wang D, Tang R, Lin H, Liu L, Xu N, Sun Y, Zhao X, Wang Z, Wang D, Mai Z, Zhou Y, Gao N, Song C, Zhu L, Wu T, Liu M, Xing G. Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing. Nat Commun 2023; 14:1068. [PMID: 36828856 PMCID: PMC9957988 DOI: 10.1038/s41467-023-36728-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation of Moore's law. However, an ideal artificial neuron possessing bio-inspired behaviors as exemplified by the requisite leaky-integrate-fire and self-reset (LIFT) functionalities within a single device is still lacking. Here, we report a new type of spiking neuron with LIFT characteristics by manipulating the magnetic domain wall motion in a synthetic antiferromagnetic (SAF) heterostructure. We validate the mechanism of Joule heating modulated competition between the Ruderman-Kittel-Kasuya-Yosida interaction and the built-in field in the SAF device, enabling it with a firing rate up to 17 MHz and energy consumption of 486 fJ/spike. A spiking neuron circuit is implemented with a latency of 170 ps and power consumption of 90.99 μW. Moreover, the winner-takes-all is executed with a current ratio >104 between activated and inhibited neurons. We further establish a two-layer spiking neural network based on the developed spintronic LIFT neurons. The architecture achieves 88.5% accuracy on the handwritten digit database benchmark. Our studies corroborate the circuit compatibility of the spintronic neurons and their great potential in the field of intelligent devices and neuromorphic computing.
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Affiliation(s)
- Di Wang
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Ruifeng Tang
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Huai Lin
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Long Liu
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Nuo Xu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94720, USA
| | - Yan Sun
- Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Xuefeng Zhao
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Ziwei Wang
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Dandan Wang
- Jiufengshan Laboratory, Wuhan, 430206, Hubei, China
| | - Zhihong Mai
- Jiufengshan Laboratory, Wuhan, 430206, Hubei, China
| | - Yongjian Zhou
- Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, 100084, Beijing, China
| | - Nan Gao
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Cheng Song
- Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, 100084, Beijing, China
| | - Lijun Zhu
- State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
| | - Tom Wu
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Ming Liu
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Guozhong Xing
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, Anhui, China.
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