Stack-Engineered Mode Selection in PtMn/(Co/Pd) n Multilayers Enables Deterministic Analog Spin–Orbit Torque Synapses
Source abstract
Abstract
High Resolution Image Download MS PowerPoint Slide Spin–orbit-torque (SOT) devices that support both binary and analog switching can bridge spintronic memory and neuromorphic computing, provided the switching mode can be deliberately assigned. Here, we demonstrate that in PtMn/(Co/Pd)n multilayers, the Co/Pd repeat number, n Co/Pd, serves as a material parameter that determines the reversal mechanism and switching mode. For n Co/Pd ≤ 7, magnetization reversal is governed by nucleation followed by domain-wall propagation, resulting in binary switching that can undergo a transition to analog behavior through electrical conditioning. For n Co/Pd ≥ 8, increased structural modulation in the as-deposited state suppresses domain-wall propagation and stabilizes nucleation-dominated analog switching without additional processing. Applying controlled current conditioning to these high-n Co/Pd stacks produces a hybrid state with smoother long-term potentiation and depression, an expanded number of intermediate states, and neuromorphic classification accuracy exceeding 97%. These results establish stack design and hybrid tuning as scalable strategies for energy-efficient analog SOT synapses in neuromorphic hardware.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Magnetic properties of thin films · Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials Science and Engineering
National Tsing Hua University
Centre for Applied Research in Electronics
Indian Institute of Technology Delhi
Instrumentation Center
Department of Engineering and System Science
Department of Materials Science & Engineering
National Yang-Ming Chiao-Tung University
Institute of Space Systems Engineering
College of Semiconductor Research
Funding
National Science and Technology Council
References (48)
Show source-provided references
1. 10.1080/21663831.2022.214780310.1080/21663831.2022.2147803 ↗
2. 10.1038/nmat456610.1038/nmat4566 ↗
3. 10.1038/s41928-019-0360-910.1038/s41928-019-0360-9 ↗
4. 10.1038/nature2301110.1038/nature23011 ↗
5. 10.1002/adma.20160157510.1002/adma.201601575 ↗
6. 10.1109/JPROC.2016.259715210.1109/JPROC.2016.2597152 ↗
7. 10.1038/nmat382310.1038/nmat3823 ↗
8. 10.1002/advs.20240787010.1002/advs.202407870 ↗
9. 10.1002/adma.20190063610.1002/adma.201900636 ↗
10. 10.1021/acsanm.2c0409410.1021/acsanm.2c04094 ↗
11. 10.1063/5.007343010.1063/5.0073430 ↗
12. 10.1063/1.504179310.1063/1.5041793 ↗
13. 10.1016/j.isci.2020.10161410.1016/j.isci.2020.101614 ↗
14. 10.1038/nmat261310.1038/nmat2613 ↗
15. 10.1002/adfm.20240467910.1002/adfm.202404679 ↗
16. 10.1038/nature1030910.1038/nature10309 ↗
17. 10.1126/science.121819710.1126/science.1218197 ↗
18. 10.1002/adma.20230085310.1002/adma.202300853 ↗
19. 10.1038/s41563-019-0289-410.1038/s41563-019-0289-4 ↗
20. 10.1063/1.493744310.1063/1.4937443 ↗
21. 10.1021/acs.nanolett.4c0171210.1021/acs.nanolett.4c01712 ↗
22. 10.1002/aelm.20230047210.1002/aelm.202300472 ↗
23. 10.1103/PhysRevApplied.17.01401610.1103/PhysRevApplied.17.014016 ↗
24. 10.1038/s41427-021-00282-310.1038/s41427-021-00282-3 ↗
25. 10.1063/1.497783810.1063/1.4977838 ↗
26. 10.1021/acsami.5c1394110.1021/acsami.5c13941 ↗
27. 10.1103/22yc-b15910.1103/22yc-b159 ↗
28. 10.1021/acsami.5c0488510.1021/acsami.5c04885 ↗
29. 10.1063/5.003566710.1063/5.0035667 ↗
30. 10.1002/adma.20210367210.1002/adma.202103672 ↗
31. 10.1002/adfm.20180810410.1002/adfm.201808104 ↗
32. 10.1021/acs.nanolett.2c0240910.1021/acs.nanolett.2c02409 ↗
33. 10.1002/adfm.20210787010.1002/adfm.202107870 ↗
34. 10.1021/acs.nanolett.5c0110010.1021/acs.nanolett.5c01100 ↗
35. 10.1016/S0304-8853(01)00658-810.1016/S0304-8853(01)00658-8 ↗
36. 10.1063/5.016650310.1063/5.0166503 ↗
37. 10.1103/PhysRevB.93.22040510.1103/PhysRevB.93.220405 ↗
38. 10.1063/1.134280010.1063/1.1342800 ↗
39. 10.1002/adfm.20211256110.1002/adfm.202112561 ↗
40. 10.1038/s44306-025-00071-610.1038/s44306-025-00071-6 ↗
41. 10.1016/j.jmmm.2024.17274810.1016/j.jmmm.2024.172748 ↗
42. 10.1016/j.surfin.2025.10761810.1016/j.surfin.2025.107618 ↗
43. 10.1063/1.36982910.1063/1.369829 ↗
44. 10.1016/S0304-8853(99)00651-410.1016/S0304-8853(99)00651-4 ↗
45. 10.1109/TCAD.2018.278972310.1109/TCAD.2018.2789723 ↗
46. Ananthanarayanan, R.; Esser, S. K.; Modha, D. S. Synaptic weight normalized spiking neuronal networks; US 8,655,813 B2, 2014.
47. 10.1038/s41598-018-21057-x10.1038/s41598-018-21057-x ↗
48. Anwani N. · 2015 · 1