Improvement of Conductance Modulation Linearity in a Cu2+-Doped KNbO3 Memristor through the Increase of the Number of Oxygen Vacancies
Source abstract
Abstract
-doped KN (CKN) film was mainly influenced by the redox process of oxygen vacancies. Therefore, the CKN film exhibited improved conductance modulation linearity, confirming that the linearity of conductance modulation can be improved by increasing the number of oxygen vacancies in the memristor. This method can be applied to other memristors to improve the linearity of conductance modulation. The CKN memristor also provides excellent biological synaptic characteristics for neuromorphic computing systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Photoreceptor and optogenetics research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
Show source-provided references
1. 10.1021/nl904092h10.1021/nl904092h ↗
2. 10.1002/aelm.20160009010.1002/aelm.201600090 ↗
3. 10.1109/5.5835610.1109/5.58356 ↗
4. 10.1038/nmat341510.1038/nmat3415 ↗
5. 10.1021/nl201040y10.1021/nl201040y ↗
6. 10.1109/TNANO.2012.220212510.1109/TNANO.2012.2202125 ↗
7. 10.1109/TBCAS.2016.253379810.1109/TBCAS.2016.2533798 ↗
8. 10.1038/nmat305410.1038/nmat3054 ↗
9. 10.1021/acs.nanolett.6b0530810.1021/acs.nanolett.6b05308 ↗
10. 10.1021/nl073225h10.1021/nl073225h ↗
11. 10.1038/36310.1038/363 ↗
12. 10.1523/JNEUROSCI.3062-05.200610.1523/JNEUROSCI.3062-05.2006 ↗
13. 10.1016/S0896-6273(00)80685-610.1016/S0896-6273(00)80685-6 ↗
14. 10.1038/361031a010.1038/361031a0 ↗
15. 10.1021/nn202983n10.1021/nn202983n ↗
16. 10.1038/nn.221510.1038/nn.2215 ↗
17. 10.3389/fncom.2011.0003810.3389/fncom.2011.00038 ↗
18. Gerstner W. · 200210.1017/CBO9780511815706 ↗
19. 10.1523/JNEUROSCI.18-24-10464.199810.1523/JNEUROSCI.18-24-10464.1998 ↗
20. 10.1021/acsnano.7b0334710.1021/acsnano.7b03347 ↗
21. 10.1002/adfm.20180055310.1002/adfm.201800553 ↗
22. 10.1039/C6NR00476H10.1039/C6NR00476H ↗
23. 10.1038/s41467-018-04484-210.1038/s41467-018-04484-2 ↗
24. 10.1088/1361-6463/aac8a510.1088/1361-6463/aac8a5 ↗
25. 10.1063/1.493481810.1063/1.4934818 ↗
26. 10.1021/acsami.7b1151910.1021/acsami.7b11519 ↗
27. 10.1021/acsami.8b0455010.1021/acsami.8b04550 ↗
28. 10.1103/RevModPhys.72.31510.1103/RevModPhys.72.315 ↗
29. 10.1016/j.surfrep.2003.10.00110.1016/j.surfrep.2003.10.001 ↗
30. 10.1016/S0166-2236(02)02269-510.1016/S0166-2236(02)02269-5 ↗
31. 10.1037/0033-295X.103.4.73410.1037/0033-295X.103.4.734 ↗
32. 10.1111/jace.1322510.1111/jace.13225 ↗
33. 10.1021/nn503464q10.1021/nn503464q ↗
34. 10.1038/s41467-018-07572-510.1038/s41467-018-07572-5 ↗
35. Querlioz, D.; Dollfus, P.; Bichler, O.; Gamrat, C. In Learning with Memristive Devices: How Should We Model Their Behavior? IEEE/ACM International Symposium on Nanoscale Architectures, 2011; pp 150–156.10.1109/NANOARCH.2011.5941497 ↗
36. 10.1021/acs.nanolett.5b0069710.1021/acs.nanolett.5b00697 ↗