Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing
Source abstract
Abstract
Vanadium oxides are widely studied phase change materials for brain-inspired computing architectures. Systems like VO 2 and V 2 O 3 exhibit first-order metal−insulator transitions (MITs) with hysteresis and percolative switching, increasing stochasticity and device variability. Here, we focus on the less explored Magnéli phase V 4 O 7, which undergoes a continuous, non-hysteretic, second-order MIT. This surprisingly enables highly reproducible volatile resistive switching in spiking-neuron-type devices. We synthesize V 4 O 7 films, characterize their structural and transport properties, and demonstrate voltage and current-driven threshold switching with electrothermal feedback. In a Pearson–Anson oscillator, V 4 O 7 devices produce stable, tunable spiking across 20–200 kHz, with consistent operation among multiple devices. We introduce a numerical analog leaky-integrate-and-fire (aLIF) model that captures waveform shapes and their dependence on load resistance, temperature, and voltage. These findings suggest that second-order MIT materials like V 4 O 7 are promising for deterministic, scalable spiking neuron arrays for neuromorphic computing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Neural Networks and Reservoir Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physics, Center for Advanced Nanoscience
University of California
General Atomics
Materials Science and Engineering Program
University of California−San Diego
Department of Electrical Engineering and Computer Science
University of Tennessee
Integrative Neuroscience and Cognition Centers
Université Paris Cité, CNRS
Funding
Energy Frontier Research Centers · National Science Foundation (NSF)
References (78)
Show source-provided references
1. 10.1002/aisy.20240019110.1002/aisy.202400191 ↗
2. 10.1038/s43588-021-00184-y10.1038/s43588-021-00184-y ↗
3. 10.1002/aelm.20230083910.1002/aelm.202300839 ↗
4. 10.1002/admt.20180058910.1002/admt.201800589 ↗
5. 10.3390/brainsci1207086310.3390/brainsci12070863 ↗
6. 10.1007/978-3-031-66146-4_310.1007/978-3-031-66146-4_3 ↗
7. 10.14529/jsfi23020610.14529/jsfi230206 ↗
8. 10.1016/j.neunet.2018.12.00210.1016/j.neunet.2018.12.002 ↗
9. 10.1016/j.mattod.2025.02.00810.1016/j.mattod.2025.02.008 ↗
10. 10.1002/adma.20220529410.1002/adma.202205294 ↗
11. 10.1109/82.80347310.1109/82.803473 ↗
12. 10.1080/23746149.2016.125958510.1080/23746149.2016.1259585 ↗
13. 10.1002/adfm.20220410210.1002/adfm.202204102 ↗
14. 10.1088/2634-4386/ace64c10.1088/2634-4386/ace64c ↗
15. 10.1063/5.018477410.1063/5.0184774 ↗
16. 10.1038/s41598-019-47348-510.1038/s41598-019-47348-5 ↗
17. 10.1109/JPROC.2015.243191410.1109/JPROC.2015.2431914 ↗
18. 10.1063/5.009420510.1063/5.0094205 ↗
19. 10.1063/1.504780010.1063/1.5047800 ↗
20. 10.1109/MNANO.2022.314144310.1109/MNANO.2022.3141443 ↗
21. 10.1109/TCSII.2020.298005410.1109/TCSII.2020.2980054 ↗
22. 10.3390/electronics810106510.3390/electronics8101065 ↗
23. 10.1038/s41467-022-31747-w10.1038/s41467-022-31747-w ↗
24. 10.1063/5.000413910.1063/5.0004139 ↗
25. 10.1002/aisy.20200006610.1002/aisy.202000066 ↗
26. 10.1016/j.device.2024.10064510.1016/j.device.2024.100645 ↗
27. 10.1109/TED.2022.315246810.1109/TED.2022.3152468 ↗
28. 10.1063/5.012348610.1063/5.0123486 ↗
29. 10.1002/advs.20220111710.1002/advs.202201117 ↗
30. 10.1002/admt.20240089510.1002/admt.202400895 ↗
31. 10.1063/5.003586510.1063/5.0035865 ↗
32. 10.1063/1.355675610.1063/1.3556756 ↗
33. 10.1515/nanoph-2022-015310.1515/nanoph-2022-0153 ↗
34. 10.1103/PhysRevE.87.01212810.1103/PhysRevE.87.012128 ↗
35. 10.1038/s41467-023-39430-410.1038/s41467-023-39430-4 ↗
36. 10.1021/acs.nanolett.9b0155410.1021/acs.nanolett.9b01554 ↗
37. 10.1002/adma.20220847710.1002/adma.202208477 ↗
38. 10.1002/adma.20240090410.1002/adma.202400904 ↗
39. 10.1103/PhysRevX.12.01102510.1103/PhysRevX.12.011025 ↗
40. 10.1134/S106378340911006710.1134/S1063783409110067 ↗
41. 10.1002/aelm.20240053910.1002/aelm.202400539 ↗
42. 10.1063/5.022323010.1063/5.0223230 ↗
43. 10.3390/coatings1203029110.3390/coatings12030291 ↗
44. 10.1038/s41467-018-07052-w10.1038/s41467-018-07052-w ↗
45. 10.1038/s41699-024-00509-110.1038/s41699-024-00509-1 ↗
46. 10.1016/0022-4596(73)90233-810.1016/0022-4596(73)90233-8 ↗
47. 10.1021/acsnano.5c1786910.1021/acsnano.5c17869 ↗
48. 10.1002/adma.20220545110.1002/adma.202205451 ↗
49. 10.1002/adma.20220309710.1002/adma.202203097 ↗
50. 10.1002/adma.20230535310.1002/adma.202305353 ↗
51. 10.3389/fnins.2018.0021010.3389/fnins.2018.00210 ↗
52. 10.1021/acs.nanolett.4c0252110.1021/acs.nanolett.4c02521 ↗
53. 10.1103/PhysRevB.104.15510110.1103/PhysRevB.104.155101 ↗
54. 10.1103/PhysRevApplied.23.03403010.1103/PhysRevApplied.23.034030 ↗
55. 10.1038/8142610.1038/81426 ↗
56. 10.1016/j.chaos.2023.11405810.1016/j.chaos.2023.114058 ↗
57. 10.1006/bulm.1999.016210.1006/bulm.1999.0162 ↗
58. LiJ. New Techniques to Manipulate Quantum Materials for Neuromorphic Computing. Ph.D. Thesis, 2024. https://www.proquest.com/docview/3124487947/abstract/6E32CA47D53E4868PQ/1 (accessed April 2026)
59. 10.1103/PhysRev.56.97810.1103/PhysRev.56.978 ↗
60. 10.1021/acsami.0c1832710.1021/acsami.0c18327 ↗
61. 10.1080/0144235850935335110.1080/01442358509353351 ↗
62. 10.1002/pssa.221037025310.1002/pssa.2210370253 ↗
63. 10.7567/jjap.54.05320110.7567/jjap.54.053201 ↗
64. 10.1103/PhysRevApplied.22.01402110.1103/PhysRevApplied.22.014021 ↗
65. 10.1038/s41467-020-16752-110.1038/s41467-020-16752-1 ↗
66. 10.1103/PhysRevResearch.5.01310810.1103/PhysRevResearch.5.013108 ↗
67. 10.1021/acsaelm.5c0172110.1021/acsaelm.5c01721 ↗
68. 10.1002/aelm.20240093810.1002/aelm.202400938 ↗
69. 10.3891/acta.chem.scand.08-159910.3891/acta.chem.scand.08-1599 ↗
70. 10.1016/j.jallcom.2023.17262010.1016/j.jallcom.2023.172620 ↗
71. 10.1021/acsami.5c0268210.1021/acsami.5c02682 ↗
72. 10.1016/0025-5408(70)90050-410.1016/0025-5408(70)90050-4 ↗
73. 10.1021/acs.nanolett.4c0018410.1021/acs.nanolett.4c00184 ↗
74. 10.1016/j.jallcom.2025.18419010.1016/j.jallcom.2025.184190 ↗
75. 10.1039/C3NR01176C10.1039/C3NR01176C ↗
76. 10.1038/s41467-021-23184-y10.1038/s41467-021-23184-y ↗
77. 10.1109/JETCAS.2015.242649210.1109/JETCAS.2015.2426492 ↗
78. 10.1109/TED.2018.286612710.1109/TED.2018.2866127 ↗