Array-level MXene electrode flexible long-term-plasticity synaptic transistors
Source abstract
Abstract
MXene electrodes provide protons from surface functional groups to fill the defects of the dielectric layer to achieve long-term synaptic plasticity. Furthermore, the method of patterning MXene electrodes enables both a high level of integration and array-level patterning, which had been highly challenging, especially on flexible substrates. Additionally, the matched work functions of MXene and semiconducting carbon nanotubes enable Ohmic contact for the metal-semiconductor junction. Furthermore, an MXene electrode flexible synaptic transistor (MEFST) array is made possible for next-generation 3D perception systems to achieve artificial neuromorphic computing and temporal fusion information storage based on long-term plasticity, leading to a high accuracy of 93.8% for recognising handwritten digits. Long-term plasticity allows rapid identification upon the reappearance of the same target without needing re-iteration. The MEFST array holds immense application potential for neuromorphic electronic skin, intelligent wearable electronics and edge computing.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · MXene and MAX Phase Materials · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
Funding
National Natural Science Foundation of China · National Key Research and Development Program of China · Chinese University of Hong Kong, Shenzhen
References (36)
Show source-provided references
1. Tang · Adv. Mater. · 2019 · 31 · 190276110.1002/adma.201902761 ↗
2. von Neumann · IEEE Ann. Hist. Comput. · 1993 · 15 · 2710.1109/85.238389 ↗
3. Marshall · Nat. Neurosci. · 2022 · 25 · 149210.1038/s41593-022-01165-8 ↗
4. Asumbisa · Nat. Commun. · 2022 · 13 · 548310.1038/s41467-022-33204-0 ↗
5. Street · Mater. Today · 2006 · 9 · 3210.1016/S1369-7021(06)71445-6 ↗
6. McAlpine · Nat. Mater. · 2007 · 6 · 37910.1038/nmat1891 ↗
7. Sekitani · Science · 2009 · 326 · 151610.1126/science.1179963 ↗
8. Tee · Science · 2015 · 350 · 31310.1126/science.aaa9306 ↗
9. Oh · Nature · 2016 · 539 · 41110.1038/nature20102 ↗
10. Lee · Adv. Funct. Mater. · 2018 · 28 · 170520210.1002/adfm.201705202 ↗
11. Zhang · ACS Appl. Electron. Mater. · 2023 · 5 · 470410.1021/acsaelm.3c00238 ↗
12. Zhao · Adv. Funct. Mater. · 2021 · 31 · 210600010.1002/adfm.202106000 ↗
13. Ju · Small · 2021 · 17 · 210259510.1002/smll.202102595 ↗
14. Liu · Nat. Commun. · 2022 · 13 · 791710.1038/s41467-022-35628-0 ↗
15. Lyu · ACS Nano · 2019 · 13 · 1139210.1021/acsnano.9b04731 ↗
16. Kim · ACS Nano · 2021 · 15 · 522110.1021/acsnano.0c10471 ↗
17. Tang · Adv. Funct. Mater. · 2021 · 31 · 201089710.1002/adfm.202010897 ↗
18. Kim · Adv. Funct. Mater. · 2022 · 32 · 211195610.1002/adfm.202111956 ↗
19. Blioch · J. Physiol. · 1968 · 199 · 1110.1113/jphysiol.1968.sp008637 ↗
20. Indiveri · Proc. IEEE · 2015 · 103 · 137910.1109/JPROC.2015.2444094 ↗
21. Liu · J. Am. Chem. Soc. · 2016 · 138 · 1585310.1021/jacs.6b10834 ↗
22. Liu · Adv. Mater. · 2024 · 36 · 240462610.1002/adma.202404626 ↗
23. Zhu · Appl. Phys. Rev. · 2020 · 7 · 01131210.1063/1.5118217 ↗
24. Qiu · ACS Nano · 2023 · 17 · 1265210.1021/acsnano.3c03212 ↗
25. Huang · Carbon · 2022 · 189 · 38610.1016/j.carbon.2021.12.081 ↗
26. Wang · Adv. Mater. · 2018 · 30 · 180396110.1002/adma.201803961 ↗
27. Zucker · Annu. Rev. Physiol. · 2002 · 64 · 35510.1146/annurev.physiol.64.092501.114547 ↗
28. Hu · Appl. Phys. Lett. · 2013 · 102 · 18351010.1063/1.4804374 ↗
29. Yang · Nat. Commun. · 2014 · 5 · 1
30. C.Pai-Yu , P.Xiaochen and Y.Shimeng , 2017 IEEE International Electron Devices Meeting (IEDM) , 2017 , 6.1.16.1.4
31. Li · IEEE Sig. Proc. Magaz. · 2012 · 29 · 141
32. Zhang · Appl. Phys. Rev. · 2020 · 7 · 01131210.1063/1.5118217 ↗
33. Smidstrup · J. Phys.: Condens. Matter · 2019 · 32 · 015901
34. Perdew · Phys. Rev. Lett. · 1996 · 77 · 386510.1103/PhysRevLett.77.3865 ↗
35. Van Setten · Comput. Phys. Commun. · 2018 · 226 · 3910.1016/j.cpc.2018.01.012 ↗
36. Grimme · J. Comput. Chem. · 2006 · 27 · 178710.1002/jcc.20495 ↗