journal article·2023-12-31Open access

Emerging electrolyte-gated transistors for neuromorphic perception

Cui Sun, Xuerong Liu, Qian Jiang, Xiaoyu Ye, Xiaojian Zhu, Run-Wei Li

Abstract

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Conducting polymers and applications

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

Affiliations

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Funding

National Key Research and Development Project · National Natural Science Foundation of China · Youth Innovation Promotion Association of the CAS · Natural Science Foundation of Zhejiang Province · Key Research and Development Project of Zhejiang Province · The Instrument Developing Project of the Chinese Academy of Sciences · Ningbo Natural Science Foundation · C. Wong Education Foundation

References (100+)

Show source-provided references
1. 10.1002/adma.20190243410.1002/adma.201902434 ↗
2. 10.1021/acsnano.0c1004910.1021/acsnano.0c10049 ↗
3. 10.1002/adma.20190355810.1002/adma.201903558 ↗
4. 10.1002/aisy.20200016210.1002/aisy.202000162 ↗
5. 10.1002/adma.20190276110.1002/adma.201902761 ↗
6. 10.1016/j.neuron.2012.08.01910.1016/j.neuron.2012.08.019 ↗
7. 10.1002/advs.20210612410.1002/advs.202106124 ↗
8. 10.1002/adma.20200590210.1002/adma.202005902 ↗
9. 10.1002/aisy.20190011810.1002/aisy.201900118 ↗
10. 10.1126/scirobotics.aat042910.1126/scirobotics.aat0429 ↗
11. 10.1002/smll.20180052710.1002/smll.201800527 ↗
12. 10.1038/nature1400210.1038/nature14002 ↗
13. 10.1126/science.125464210.1126/science.1254642 ↗
14. 10.1016/j.conb.2010.03.00710.1016/j.conb.2010.03.007 ↗
15. 10.1002/aisy.20200015610.1002/aisy.202000156 ↗
16. 10.1002/aisy.20200021010.1002/aisy.202000210 ↗
17. 10.1038/s41928-018-0103-310.1038/s41928-018-0103-3 ↗
18. 10.1002/adfm.20190370010.1002/adfm.201903700 ↗
19. 10.1063/1.512224910.1063/1.5122249 ↗
20. 10.1021/acs.jpcc.5b1210510.1021/acs.jpcc.5b12105 ↗
21. 10.1007/s10853-015-9121-y10.1007/s10853-015-9121-y ↗
22. 10.1021/acsnano.9b0954910.1021/acsnano.9b09549 ↗
23. 10.1038/s41528-021-00110-210.1038/s41528-021-00110-2 ↗
24. 10.1039/c0cc01671c10.1039/c0cc01671c ↗
25. 10.1016/j.cej.2019.12224510.1016/j.cej.2019.122245 ↗
26. 10.1002/admi.20190107410.1002/admi.201901074 ↗
27. 10.1016/j.ijbiomac.2016.11.10310.1016/j.ijbiomac.2016.11.103 ↗
28. 10.1002/adfm.20191081310.1002/adfm.201910813 ↗
29. 10.1039/C6TA02621D10.1039/C6TA02621D ↗
30. 10.1016/j.progpolymsci.2020.10122010.1016/j.progpolymsci.2020.101220 ↗
31. 10.1016/j.chempr.2019.05.00910.1016/j.chempr.2019.05.009 ↗
32. 10.1002/aenm.20170218410.1002/aenm.201702184 ↗
33. 10.1021/acsami.9b1007210.1021/acsami.9b10072 ↗
34. 10.1002/adma.20180094110.1002/adma.201800941 ↗
35. 10.1002/aelm.20210033610.1002/aelm.202100336 ↗
36. 10.1038/s43586-021-00065-810.1038/s43586-021-00065-8 ↗
37. 10.1038/natrevmats.2017.8610.1038/natrevmats.2017.86 ↗
38. 10.1002/adma.20120279010.1002/adma.201202790 ↗
39. 10.1039/C8TC00530C10.1039/C8TC00530C ↗
40. 10.1002/adfm.20210109910.1002/adfm.202101099 ↗
41. 10.1002/adma.20180154810.1002/adma.201801548 ↗
42. 10.1002/adfm.20060123910.1002/adfm.200601239 ↗
43. 10.1016/j.orgel.2018.09.01010.1016/j.orgel.2018.09.010 ↗
44. 10.1039/C8TC03185A10.1039/C8TC03185A ↗
45. 10.1038/nmat485610.1038/nmat4856 ↗
46. 10.1002/adma.20150367410.1002/adma.201503674 ↗
47. 10.1002/aelm.20227001410.1002/aelm.202270014 ↗
48. 10.1002/adma.20200301810.1002/adma.202003018 ↗
49. 10.1002/smll.20190747210.1002/smll.201907472 ↗
50. 10.1126/sciadv.150132610.1126/sciadv.1501326 ↗
51. 10.1002/adfm.20210195110.1002/adfm.202101951 ↗
52. 10.1016/j.mtphys.2021.10039310.1016/j.mtphys.2021.100393 ↗
53. 10.1002/adma.20200465910.1002/adma.202004659 ↗
54. 10.1002/adfm.20180417010.1002/adfm.201804170 ↗
55. 10.1002/adfm.20180402510.1002/adfm.201804025 ↗
56. 10.1021/acsami.0c1432510.1021/acsami.0c14325 ↗
57. 10.1021/acsami.9b2236910.1021/acsami.9b22369 ↗
58. 10.1002/adma.20200646910.1002/adma.202006469 ↗
59. 10.1038/sj.npp.130155910.1038/sj.npp.1301559 ↗
60. 10.1002/aelm.20190028710.1002/aelm.201900287 ↗
61. 10.1523/JNEUROSCI.16-18-05661.199610.1523/JNEUROSCI.16-18-05661.1996 ↗
62. 10.1126/sciadv.abg945010.1126/sciadv.abg9450 ↗
63. 10.1002/pssr.20197002510.1002/pssr.201970025 ↗
64. Atkinson RC, Shiffrin RM. Human memory: a proposed system and its control processes. scientists making a difference. UK: Cambridge University Press; 2016.
65. 10.1021/acsami.8b0327410.1021/acsami.8b03274 ↗
66. 10.1038/s41467-021-22680-510.1038/s41467-021-22680-5 ↗
67. 10.1021/acsnano.1c0848210.1021/acsnano.1c08482 ↗
68. 10.1523/JNEUROSCI.20-23-08812.200010.1523/JNEUROSCI.20-23-08812.2000 ↗
69. 10.1002/adfm.20210004210.1002/adfm.202100042 ↗
70. 10.1021/acsami.0c2204710.1021/acsami.0c22047 ↗
71. 10.1002/adfm.20210006910.1002/adfm.202100069 ↗
72. 10.1039/D1TC04827A10.1039/D1TC04827A ↗
73. 10.1002/adfm.20220104810.1002/adfm.202201048 ↗
74. 10.1038/381526a010.1038/381526a0 ↗
75. Hu L, Li L, Chang KC, et al. Ultrasensitive freestanding and mechanically durable artificial synapse with attojoule power based on Na-salt doped polymer for biocompatible neuromorphic interface. Adv Funct Mater. 2021;31:2106019.
76. 10.1002/adfm.20210844010.1002/adfm.202108440 ↗
77. 10.1039/C6TC02228F10.1039/C6TC02228F ↗
78. 10.1038/s41467-020-15158-310.1038/s41467-020-15158-3 ↗
79. 10.1038/nrn335310.1038/nrn3353 ↗
80. 10.1016/j.orgel.2020.10578210.1016/j.orgel.2020.105782 ↗
81. 10.1002/adma.20180022010.1002/adma.201800220 ↗
82. 10.1002/adma.20150357510.1002/adma.201503575 ↗
83. 10.1002/aelm.20220046310.1002/aelm.202200463 ↗
84. 10.1039/C6NR08024C10.1039/C6NR08024C ↗
85. 10.1038/s41467-022-32078-610.1038/s41467-022-32078-6 ↗
86. 10.1038/nrn235610.1038/nrn2356 ↗
87. 10.1016/j.orgel.2019.07.02810.1016/j.orgel.2019.07.028 ↗
88. 10.1016/S0166-2236(96)80018-X10.1016/S0166-2236(96)80018-X ↗
89. 10.1002/smll.20180543110.1002/smll.201805431 ↗
90. 10.1021/acsnano.8b0590310.1021/acsnano.8b05903 ↗
91. 10.1039/D2NA00146B10.1039/D2NA00146B ↗
92. 10.1038/4657410.1038/46574 ↗
93. 10.1038/s41578-022-00434-z10.1038/s41578-022-00434-z ↗
94. 10.1038/3503619110.1038/35036191 ↗
95. 10.1098/rstb.2013.014810.1098/rstb.2013.0148 ↗
96. 10.1002/adma.20150320210.1002/adma.201503202 ↗
97. 10.1002/advs.20190348010.1002/advs.201903480 ↗
98. 10.1002/aisy.20200009610.1002/aisy.202000096 ↗
99. 10.1002/advs.20210297310.1002/advs.202102973 ↗
100. 10.1109/LED.2021.313890710.1109/LED.2021.3138907 ↗