journal article·2025-10-08

Field-Free Spin–Orbit Torque Switching in Wedge-Shaped Pt/Co/Ta for High-Performance Neuromorphic Devices

Xi Guo, Junwei Zeng, Jijun Yun, Yalu Zuo, Li Xi, Xiaoxi Liu, Baoshan Cui

ACS Applied Materials & Interfaces· Volume 17· Issue 40
Publisher page

Abstract

In perpendicularly magnetized heavy-metal/ferromagnetic heterostructures, the spin-orbit torque-induced multilevel switching of magnetic moment offers promising potential for nonvolatile multilevel information storage. Compared with traditional binary memory architectures, this approach provides improved data density and lower power consumption. However, the necessity of an additional in-plane magnetic field to break magnetic symmetry in perpendicularly magnetized heterostructures impedes the integration of spin-orbit torque-driven magnetization switching devices. This work investigates field-free spin-orbit torque switching in obliquely sputtered Pt/Co/Ta heterostructures, demonstrating that the oblique deposition angle determines the tilt of magnetic moments, thereby enabling field-free switching. By adjusting the width and amplitude of current pulses, we implemented multiple resistance states, mimicking synaptic behaviors such as long-term potentiation/long-term depression and excitatory/inhibitory postsynaptic potentials. Besides, linear optimization of magnetization switching curves revealed their compatibility with the rectified linear unit activation function, further highlighting the versatility of this approach for application in artificial neural networks. Finally, a fully connected convolutional neural network utilizing field-free spin-orbit torque devices achieved an impressive classification accuracy of 93.38% on the CIFAR-10 data set, demonstrating the practical feasibility of this technology in advanced machine learning tasks.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Magnetic properties of thin films

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Physical Science and Technology

Lanzhou University

Institute for Quantum Information & State Key Laboratory of High-Performance Computing, College of Computer

National University of Defense Technology

Shaanxi Key Laboratory of Condensed Matter Structures and Properties, and MOE Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, School of Physical Science and Technology

Northwestern Polytechnical University

Funding

Basic and Applied Basic Research Foundation of Guangdong Province · Songshan Lake Materials Laboratory · Lanzhou University · Natural Science Basic Research Program of Shaanxi Province · Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China · Natural Science Foundation of Gansu Province · High-end Talent Funding of Lanzhou City · Science and Technology Foundation for Youths of Gansu Province · Key Talent Project of Gansu Province · State Key Laboratory of Spintronics Devices and Technologies

References (64)

Show source-provided references
1. 10.1016/j.is.2014.07.00610.1016/j.is.2014.07.006 ↗
2. 10.1038/s41928-020-0435-710.1038/s41928-020-0435-7 ↗
3. 10.3390/computers1307017410.3390/computers13070174 ↗
4. 10.1063/1.511821710.1063/1.5118217 ↗
5. 10.1016/j.heliyon.2018.e0093810.1016/j.heliyon.2018.e00938 ↗
6. 10.1038/s41586-020-1942-410.1038/s41586-020-1942-4 ↗
7. 10.1038/nature2301110.1038/nature23011 ↗
8. 10.1038/s41467-022-28303-x10.1038/s41467-022-28303-x ↗
9. 10.1038/s41586-021-04196-610.1038/s41586-021-04196-6 ↗
10. 10.1038/s41928-020-0385-010.1038/s41928-020-0385-0 ↗
11. 10.1038/s41467-018-04484-210.1038/s41467-018-04484-2 ↗
12. 10.1038/nature0693210.1038/nature06932 ↗
13. 10.1002/adfm.20220236610.1002/adfm.202202366 ↗
14. 10.1038/ncomms1473610.1038/ncomms14736 ↗
15. 10.1038/s41586-018-0180-510.1038/s41586-018-0180-5 ↗
16. 10.1109/TCAD.2020.300018510.1109/TCAD.2020.3000185 ↗
17. 10.1142/S201032471240002410.1142/S2010324712400024 ↗
18. 10.1063/5.006299310.1063/5.0062993 ↗
19. 10.1088/1361-6463/adb9f710.1088/1361-6463/adb9f7 ↗
20. 10.1002/adfm.20210787010.1002/adfm.202107870 ↗
21. 10.1002/adfm.20190909210.1002/adfm.201909092 ↗
22. 10.1002/adma.20190063610.1002/adma.201900636 ↗
23. 10.1038/s41467-024-48631-410.1038/s41467-024-48631-4 ↗
24. 10.1016/j.jmmm.2022.17011110.1016/j.jmmm.2022.170111 ↗
25. 10.1021/acsami.5c0488510.1021/acsami.5c04885 ↗
26. 10.1002/adma.20200361010.1002/adma.202003610 ↗
27. 10.1038/nature1030910.1038/nature10309 ↗
28. 10.1126/science.121819710.1126/science.1218197 ↗
29. 10.1038/nmat331110.1038/nmat3311 ↗
30. 10.1038/nnano.2013.14510.1038/nnano.2013.145 ↗
31. 10.1103/PhysRevB.91.22441310.1103/PhysRevB.91.224413 ↗
32. 10.1109/TMAG.2021.307858310.1109/TMAG.2021.3078583 ↗
33. 10.1038/nnano.2014.9410.1038/nnano.2014.94 ↗
34. 10.1021/acsami.9b1362210.1021/acsami.9b13622 ↗
35. 10.1038/nnano.2016.8410.1038/nnano.2016.84 ↗
36. 10.1038/ncomms1085410.1038/ncomms10854 ↗
37. 10.1103/PhysRevLett.129.13720110.1103/PhysRevLett.129.137201 ↗
38. 10.1038/s41467-023-38550-110.1038/s41467-023-38550-1 ↗
39. 10.1103/PhysRevApplied.10.03405210.1103/PhysRevApplied.10.034052 ↗
40. 10.1038/nmat488610.1038/nmat4886 ↗
41. 10.1038/s41467-021-27459-210.1038/s41467-021-27459-2 ↗
42. 10.1063/5.025821310.1063/5.0258213 ↗
43. 10.1002/adfm.20211256110.1002/adfm.202112561 ↗
44. 10.1103/PhysRevB.108.05441710.1103/PhysRevB.108.054417 ↗
45. 10.1103/PhysRevApplied.10.04403810.1103/PhysRevApplied.10.044038 ↗
46. 10.1021/acsnano.2c0375610.1021/acsnano.2c03756 ↗
47. 10.1103/PhysRevApplied.11.06100510.1103/PhysRevApplied.11.061005 ↗
48. 10.1103/physrevb.96.10441210.1103/physrevb.96.104412 ↗
49. 10.1088/1361-6463/ac33d910.1088/1361-6463/ac33d9 ↗
50. 10.1016/S0166-2236(96)80018-X10.1016/S0166-2236(96)80018-X ↗
51. 10.1007/978-981-13-6772-4_7610.1007/978-981-13-6772-4_76 ↗
52. Rasamoelina, A. D.; Adjailia, F.; Sinčák, P. In A Review of Activation Function for Artificial Neural Network, 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI); IEEE, 2020; pp 281–286 10.1109/SAMI48414.2020.9108717.10.1109/SAMI48414.2020.9108717 ↗
53. Thakkar, V.; Tewary, S.; Chakraborty, C. In Batch Normalization in Convolutional Neural Networks─A Comparative Study with CIFAR-10 Data; Fifth International Conference on Emerging Applications of Information Technology (EAIT); IEEE, 2018; pp 1–5 10.1109/eait.2018.8470438.10.1109/eait.2018.8470438 ↗
54. 10.1063/1.486696510.1063/1.4866965 ↗
55. 10.1038/nmat352210.1038/nmat3522 ↗
56. 10.1038/nature1453910.1038/nature14539 ↗
57. Hao, W.; Yizhou, W.; Yaqin, L.; Zhili, S. In The Role of Activation Function in CNN, 2020 2nd International Conference on Information Technology and Computer Application (ITCA); IEEE, 2020; pp 429–432 10.1109/itca52113.2020.00096.10.1109/itca52113.2020.00096 ↗
58. 10.1002/adfm.20240467910.1002/adfm.202404679 ↗
59. 10.1063/5.002225710.1063/5.0022257 ↗
60. 10.1063/1.489622510.1063/1.4896225 ↗
61. 10.1109/TBCAS.2016.252582310.1109/TBCAS.2016.2525823 ↗
62. 10.1038/s41427-021-00282-310.1038/s41427-021-00282-3 ↗
63. 10.1021/acsami.4c1340510.1021/acsami.4c13405 ↗
64. 10.1016/j.knosys.2022.10865110.1016/j.knosys.2022.108651 ↗