Robust and Low-Power-Consumption Black Phosphorus–Graphene Artificial Synaptic Devices
Source abstract
Abstract
Two-dimensional (2D) black phosphorus (BP) materials, as the most promising building blocks for the development of artificial synapses, have attracted more and more attention. However, the instability of exfoliated 2D BP structures still remains a problem in the development of artificial synapse devices. In this study, the robust and low-power-consumption artificial-synaptic-based BP was successfully manufactured. The synapse devices have high stability in the air atmosphere and do not show obvious degradation over 3 months. The obtained devices not only implement the main function of synapses but also perform the function of dendritic neural synapses and simple logical operations, revealing their very strong learning behavior. The high mobility of 2D BP as well as the coupled effect and quantum confinement effect of the graphene oxide quantum dot (GOQD) can greatly boost the performance of BP-based synapse devices, such as low power consumption (62 pW) and high sensitivity (ultrasmall stimuli at an amplitude of -20 mV). Moreover, benefiting from the GOQD and the interaction between BP and graphene, the main dominated mechanism of the BP-graphene synapse device can be the capture and release of electrons by the 2D BP and GOQD instead of the conductive filament.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · 2D Materials and Applications · Perovskite Materials and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi’an, Shaanxi 710021, People’s Republic of China
Department of Microelectronic Science and Engineering, School of Physical Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, People’s Republic of China
Funding
National Natural Science Foundation of China · Key Research and Development Projects of Shaanxi Province · Shaanxi Association for science and technology
References (71)
Show source-provided references
1. 10.1109/85.23838910.1109/85.238389 ↗
2. Comput. · IEEE Ann. Hist. · 1993 · 15 · 3
3. 10.1016/j.tins.2009.05.00110.1016/j.tins.2009.05.001 ↗
4. Kandel, E. R.; Schwartz, J. H.; Jessell, T. M.; Siegelbaum, S.; Hudspeth, A. J.; Mack, S. Principles of Neural Science; McGraw-Hill: New York, 2000; Vol. 4, pp 1227–1246.
5. 10.1038/nmat485610.1038/nmat4856 ↗
6. 10.1038/503022a10.1038/503022a ↗
7. 10.1038/nmat305410.1038/nmat3054 ↗
8. 10.1002/adma.20160316910.1002/adma.201603169 ↗
9. 10.1002/adma.20180032710.1002/adma.201800327 ↗
10. 10.1063/5.003666710.1063/5.0036667 ↗
11. 10.1016/j.jallcom.2019.15362510.1016/j.jallcom.2019.153625 ↗
12. 10.1088/1361-6528/ab348010.1088/1361-6528/ab3480 ↗
13. 10.1186/s11671-017-1847-910.1186/s11671-017-1847-9 ↗
14. 10.1002/adts.20170003510.1002/adts.201700035 ↗
15. 10.1002/smll.20170093310.1002/smll.201700933 ↗
16. 10.1088/2053-1583/aa805e10.1088/2053-1583/aa805e ↗
17. 10.1002/adma.20160016610.1002/adma.201600166 ↗
18. 10.1002/aelm.20190101210.1002/aelm.201901012 ↗
19. 10.1039/C8TC04395G10.1039/C8TC04395G ↗
20. 10.1088/1361-6528/ab82d610.1088/1361-6528/ab82d6 ↗
21. 10.1007/s00521-019-04383-710.1007/s00521-019-04383-7 ↗
22. 10.1016/j.nanoen.2019.01.04510.1016/j.nanoen.2019.01.045 ↗
23. 10.1002/adfm.20190199110.1002/adfm.201901991 ↗
24. 10.1002/aelm.20190045810.1002/aelm.201900458 ↗
25. 10.1021/acs.nanolett.7b0434210.1021/acs.nanolett.7b04342 ↗
26. 10.1002/adma.20180666310.1002/adma.201806663 ↗
27. 10.1021/acsnano.5b0555610.1021/acsnano.5b05556 ↗
28. 10.1038/nmat479210.1038/nmat4792 ↗
29. 10.1038/nature1238510.1038/nature12385 ↗
30. 10.1126/science.aac943910.1126/science.aac9439 ↗
31. 10.1126/science.121846110.1126/science.1218461 ↗
32. 10.1038/nnano.2014.15010.1038/nnano.2014.150 ↗
33. 10.1021/nn305913610.1021/nn3059136 ↗
34. 10.1002/adma.20140108410.1002/adma.201401084 ↗
35. 10.1088/2053-1583/ab626710.1088/2053-1583/ab6267 ↗
36. 10.1002/adfm.20150418710.1002/adfm.201504187 ↗
37. 10.1038/nnano.2015.7110.1038/nnano.2015.71 ↗
38. 10.1038/nmat429910.1038/nmat4299 ↗
39. 10.1002/adfm.20150290210.1002/adfm.201502902 ↗
40. 10.1007/s40843-016-0122-110.1007/s40843-016-0122-1 ↗
41. 10.1002/adma.20160577610.1002/adma.201605776 ↗
42. 10.1021/acs.inorgchem.8b0027810.1021/acs.inorgchem.8b00278 ↗
43. 10.1002/smll.20180420110.1002/smll.201804201 ↗
44. 10.1039/C8TA08374F10.1039/C8TA08374F ↗
45. 10.1016/j.jallcom.2019.05.34610.1016/j.jallcom.2019.05.346 ↗
46. 10.1002/smll.20190397710.1002/smll.201903977 ↗
47. 10.1021/acsnano.7b0303310.1021/acsnano.7b03033 ↗
48. 10.1073/pnas.191661011710.1073/pnas.1916610117 ↗
49. 10.1002/smll.20170075810.1002/smll.201700758 ↗
50. 10.1021/acsami.7b1159910.1021/acsami.7b11599 ↗
51. 10.1021/jacs.8b0215610.1021/jacs.8b02156 ↗
52. 10.1021/acsnano.6b0629310.1021/acsnano.6b06293 ↗
53. 10.1002/anie.20140940010.1002/anie.201409400 ↗
54. 10.1039/C7TC03037A10.1039/C7TC03037A ↗
55. 10.1021/jacs.5b0602510.1021/jacs.5b06025 ↗
56. 10.1038/nature0301010.1038/nature03010 ↗
57. 10.3389/fncom.2013.0018810.3389/fncom.2013.00188 ↗
58. 10.1038/nmat475610.1038/nmat4756 ↗
59. 10.1002/aelm.20190046710.1002/aelm.201900467 ↗
60. 10.1088/0957-4484/20/19/19520310.1088/0957-4484/20/19/195203 ↗
61. 10.1002/adfm.20170532010.1002/adfm.201705320 ↗
62. 10.1002/adfm.20200723210.1002/adfm.202007232 ↗
63. 10.1038/nn.221510.1038/nn.2215 ↗
64. 10.1073/pnas.110616110810.1073/pnas.1106161108 ↗
65. 10.1039/C8NR05336G10.1039/C8NR05336G ↗
66. 10.1016/j.matdes.2019.10840010.1016/j.matdes.2019.108400 ↗
67. 10.1038/s41598-017-09762-510.1038/s41598-017-09762-5 ↗
68. 10.1002/adma.20190002110.1002/adma.201900021 ↗
69. 10.1063/1.504241910.1063/1.5042419 ↗
70. 10.1021/acsami.0c0060210.1021/acsami.0c00602 ↗
71. 10.1016/j.jpowsour.2016.04.02510.1016/j.jpowsour.2016.04.025 ↗