Pseudo-transistors for emerging neuromorphic electronics
Source abstract
Abstract
Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Nanjing University of Posts & Telecommunications
University of Electronic Science and Technology of China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Jiangsu Province
References (100+)
Show source-provided references
1. 10.1109/5.5835610.1109/5.58356 ↗
2. 10.1126/science.abg316110.1126/science.abg3161 ↗
3. 10.1109/PROC.1976.1009210.1109/PROC.1976.10092 ↗
4. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
5. 10.1002/smm2.113510.1002/smm2.1135 ↗
6. 10.1016/j.orgel.2019.07.02810.1016/j.orgel.2019.07.028 ↗
7. 10.1088/0957-4484/24/38/38200110.1088/0957-4484/24/38/382001 ↗
8. 10.1002/adma.20210802510.1002/adma.202108025 ↗
9. 10.1109/ACCESS.2020.300058910.1109/ACCESS.2020.3000589 ↗
10. 10.1126/science.125464210.1126/science.1254642 ↗
11. 10.1109/JPROC.2014.230463810.1109/JPROC.2014.2304638 ↗
12. 10.1109/MM.2018.11213035910.1109/MM.2018.112130359 ↗
13. 10.1002/adma.20160431010.1002/adma.201604310 ↗
14. 10.1002/adma.20180129110.1002/adma.201801291 ↗
15. 10.1126/sciadv.abg945010.1126/sciadv.abg9450 ↗
16. 10.1038/s41467-022-33393-810.1038/s41467-022-33393-8 ↗
17. 10.1126/sciadv.abn775310.1126/sciadv.abn7753 ↗
18. 10.1038/ncomms1519910.1038/ncomms15199 ↗
19. 10.1002/smm2.102210.1002/smm2.1022 ↗
20. 10.1038/nmat475610.1038/nmat4756 ↗
21. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
22. 10.1002/adma.20170133310.1002/adma.201701333 ↗
23. 10.1002/aelm.20160041610.1002/aelm.201600416 ↗
24. 10.1038/nature0693210.1038/nature06932 ↗
25. 10.1109/NANOARCH.2008.458579610.1109/NANOARCH.2008.4585796 ↗
26. 10.1021/nl904092h10.1021/nl904092h ↗
27. 10.1038/ncomms307210.1038/ncomms3072 ↗
28. Kuzum D, Jeyasingh RGD, Wong HSP, editors. Energy efficient programming of nanoelectronic synaptic devices for large-scale implementation of associative and temporal sequence learning. 2011 Int Electron Devices Meet. Washington, DC, USA, 2011;30.3.1–30.3.4.
29. 10.1109/TED.2015.243963510.1109/TED.2015.2439635 ↗
30. 10.1002/adma.20090368010.1002/adma.200903680 ↗
31. 10.1038/nmat305410.1038/nmat3054 ↗
32. 10.1038/nature1444110.1038/nature14441 ↗
33. 10.1038/s41586-020-1942-410.1038/s41586-020-1942-4 ↗
34. 10.1002/aelm.20220015510.1002/aelm.202200155 ↗
35. 10.1002/adma.20180288310.1002/adma.201802883 ↗
36. 10.1002/adma.20200620110.1002/adma.202006201 ↗
37. 10.1002/adma.20210437010.1002/adma.202104370 ↗
38. 10.1063/1.512491510.1063/1.5124915 ↗
39. 10.1002/adfm.20210004210.1002/adfm.202100042 ↗
40. 10.1038/s41467-020-19806-610.1038/s41467-020-19806-6 ↗
41. 10.1021/acs.nanolett.8b0402310.1021/acs.nanolett.8b04023 ↗
42. 10.1109/TETCI.2019.292178710.1109/TETCI.2019.2921787 ↗
43. 10.3389/fnins.2019.0065010.3389/fnins.2019.00650 ↗
44. 10.1002/sstr.20200002910.1002/sstr.202000029 ↗
45. 10.1038/s41928-022-00713-110.1038/s41928-022-00713-1 ↗
46. 10.1002/advs.20210349410.1002/advs.202103494 ↗
47. 10.1002/smm2.115410.1002/smm2.1154 ↗
48. 10.1021/acsnano.1c1097810.1021/acsnano.1c10978 ↗
49. 10.1126/sciadv.abl506810.1126/sciadv.abl5068 ↗
50. 10.1038/s41467-021-26314-810.1038/s41467-021-26314-8 ↗
51. 10.1002/adma.20200301810.1002/adma.202003018 ↗
52. 10.1002/adma.20190090310.1002/adma.201900903 ↗
53. 10.1021/acsami.8b0723410.1021/acsami.8b07234 ↗
54. 10.1007/s11467-021-1114-510.1007/s11467-021-1114-5 ↗
55. 10.1109/TED.2018.284987210.1109/TED.2018.2849872 ↗
56. 10.1109/LED.2017.278712410.1109/LED.2017.2787124 ↗
57. 10.1109/LED.2017.274673810.1109/LED.2017.2746738 ↗
58. 10.1109/JETCAS.2016.252859810.1109/JETCAS.2016.2528598 ↗
59. 10.1109/EDTM.2017.794759310.1109/EDTM.2017.7947593 ↗
60. 10.1109/TNS.2018.283079110.1109/TNS.2018.2830791 ↗
61. 10.1109/LED.2019.293194710.1109/LED.2019.2931947 ↗
62. 10.1109/TED.2022.321799610.1109/TED.2022.3217996 ↗
63. 10.1002/adma.20170336310.1002/adma.201703363 ↗
64. 10.1002/adma.20180707510.1002/adma.201807075 ↗
65. 10.1038/ncomms1272510.1038/ncomms12725 ↗
66. 10.1063/1.477330010.1063/1.4773300 ↗
67. 10.1063/1.483233410.1063/1.4832334 ↗
68. 10.1088/0268-1242/29/10/10401110.1088/0268-1242/29/10/104011 ↗
69. 10.1038/nature2574710.1038/nature25747 ↗
70. 10.1021/acsami.1c0858310.1021/acsami.1c08583 ↗
71. 10.1016/j.orgel.2020.10581010.1016/j.orgel.2020.105810 ↗
72. 10.1016/j.nanoen.2018.12.05710.1016/j.nanoen.2018.12.057 ↗
73. 10.1021/acsami.9b2174710.1021/acsami.9b21747 ↗
74. 10.1002/adma.20200870910.1002/adma.202008709 ↗
75. 10.1002/smll.20210317510.1002/smll.202103175 ↗
76. 10.1002/adfm.20210417410.1002/adfm.202104174 ↗
77. 10.1002/aelm.20190095510.1002/aelm.201900955 ↗
78. 10.1002/aisy.20200021010.1002/aisy.202000210 ↗
79. 10.1063/1.512224910.1063/1.5122249 ↗
80. 10.1002/adfm.20190370010.1002/adfm.201903700 ↗
81. 10.1177/107385841452982910.1177/1073858414529829 ↗
82. 10.1016/j.conb.2020.10.00710.1016/j.conb.2020.10.007 ↗
83. 10.1016/0092-8674(94)90517-710.1016/0092-8674(94)90517-7 ↗
84. 10.1523/JNEUROSCI.1697-04.200410.1523/JNEUROSCI.1697-04.2004 ↗
85. 10.3389/fnsyn.2011.0000410.3389/fnsyn.2011.00004 ↗
86. 10.1038/nature0127310.1038/nature01273 ↗
87. 10.1038/381526a010.1038/381526a0 ↗
88. 10.1002/smtd.20210055810.1002/smtd.202100558 ↗
89. 10.1021/acsnano.9b0334210.1021/acsnano.9b03342 ↗
90. 10.1021/acsnano.9b0742110.1021/acsnano.9b07421 ↗
91. 10.1002/adfm.20200368310.1002/adfm.202003683 ↗
92. 10.1039/C8CS00318A10.1039/C8CS00318A ↗
93. 10.1016/j.scib.2019.01.01610.1016/j.scib.2019.01.016 ↗
94. 10.1021/acsami.9b1425910.1021/acsami.9b14259 ↗
95. 10.1103/PhysRevLett.105.13680510.1103/PhysRevLett.105.136805 ↗
96. 10.1002/aelm.20190081810.1002/aelm.201900818 ↗
97. 10.1021/acs.nanolett.7b0485210.1021/acs.nanolett.7b04852 ↗
98. 10.1021/acsnano.8b0181010.1021/acsnano.8b01810 ↗
99. 10.1038/ncomms1495610.1038/ncomms14956 ↗
100. 10.1021/acs.nanolett.7b0219810.1021/acs.nanolett.7b02198 ↗