Spintronic memristors for computing
Abstract The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for implementing these algorithms. Memristors are programmable...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Spintronics |
| Online Access: | https://doi.org/10.1038/s44306-025-00078-z |
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| _version_ | 1850271880526692352 |
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| author | Qiming Shao Zhongrui Wang Yan Zhou Shunsuke Fukami Damien Querlioz Leon O. Chua |
| author_facet | Qiming Shao Zhongrui Wang Yan Zhou Shunsuke Fukami Damien Querlioz Leon O. Chua |
| author_sort | Qiming Shao |
| collection | DOAJ |
| description | Abstract The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for implementing these algorithms. Memristors are programmable resistors with a memory, providing a paradigm-shifting approach towards creating intelligent hardware systems to handle data-centric tasks. Spintronic nanodevices are promising choices as they are high-speed, low-power, highly scalable, robust, and capable of constructing dynamic complex systems. In this Review, we survey spintronic devices from a memristor point of view. We introduce spintronic memristors based on magnetic tunnel junctions, nanomagnet ensemble, domain walls, topological spin textures, and spin waves, which represent dramatically different state spaces. They can exhibit steady, oscillatory, stochastic, and chaotic trajectories in their state spaces, which have been exploited for in-memory logic, neuromorphic computing, stochastic and chaos computing. Finally, we discuss challenges and trends in realizing large-scale spintronic memristive systems for practical applications. |
| format | Article |
| id | doaj-art-1e34e7af442e42fbb304f42fbb7740c3 |
| institution | OA Journals |
| issn | 2948-2119 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Spintronics |
| spelling | doaj-art-1e34e7af442e42fbb304f42fbb7740c32025-08-20T01:52:03ZengNature Portfolionpj Spintronics2948-21192025-05-013112310.1038/s44306-025-00078-zSpintronic memristors for computingQiming Shao0Zhongrui Wang1Yan Zhou2Shunsuke Fukami3Damien Querlioz4Leon O. Chua5Department of Electronic and Computer Engineering, Department of Physics, Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, The Hong Kong University of Science and TechnologyACCESS – AI Chip Center for Emerging Smart Systems, Hong Kong Science ParkSchool of Science and Engineering, The Chinese University of Hong KongResearch Institute of Electrical Communication, Tohoku UniversityCentre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, CNRSDepartment of Electrical Engineering and Computer Sciences, University of CaliforniaAbstract The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for implementing these algorithms. Memristors are programmable resistors with a memory, providing a paradigm-shifting approach towards creating intelligent hardware systems to handle data-centric tasks. Spintronic nanodevices are promising choices as they are high-speed, low-power, highly scalable, robust, and capable of constructing dynamic complex systems. In this Review, we survey spintronic devices from a memristor point of view. We introduce spintronic memristors based on magnetic tunnel junctions, nanomagnet ensemble, domain walls, topological spin textures, and spin waves, which represent dramatically different state spaces. They can exhibit steady, oscillatory, stochastic, and chaotic trajectories in their state spaces, which have been exploited for in-memory logic, neuromorphic computing, stochastic and chaos computing. Finally, we discuss challenges and trends in realizing large-scale spintronic memristive systems for practical applications.https://doi.org/10.1038/s44306-025-00078-z |
| spellingShingle | Qiming Shao Zhongrui Wang Yan Zhou Shunsuke Fukami Damien Querlioz Leon O. Chua Spintronic memristors for computing npj Spintronics |
| title | Spintronic memristors for computing |
| title_full | Spintronic memristors for computing |
| title_fullStr | Spintronic memristors for computing |
| title_full_unstemmed | Spintronic memristors for computing |
| title_short | Spintronic memristors for computing |
| title_sort | spintronic memristors for computing |
| url | https://doi.org/10.1038/s44306-025-00078-z |
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