Oxygen vacancy injection-induced resistive switching in combined mobile and static gradient doped tin oxide nanorods
Source abstract
Abstract
Resistive switching devices offer a great potential for advanced computing and data storage, including neuromorphic networks and random-access memory. State-of-the-art memristors are mostly realized by a three-layer structure, which is comprised of an active metal oxide layer sandwiched between two metal electrodes. Thus, there is always an interface involving two materials differing strongly in crystallographic and electronic properties. In this study, we present a resistive switching nanorod device based on a metal oxide sandwiched between two transparent conductive oxide electrodes. Thus, the system is characterized by a different, smooth interface offering new possibilities for increased energy efficiency and transparent electronics. Antimony-doped tin oxide (ATO) is used as an electrode material. The heavily doped ATO nanorods, exhibiting a good conductivity, are produced by a templated electrochemical deposition approach of alloy particles with subsequent thermal oxidation. The process enables precise control of the doping level within the nanorods and the formation of a doping level gradient. Electrical characterization reveals that a stronger gradient between heavily doped and undoped tin oxide within the nanorods results in a more rectifying character of the junction. Three-domain nanorods consisting of an undoped tin oxide segment in between two ATO segments are utilized to introduce memristive properties into the nanorod device. The resistive switching of these nanorods can be attributed to an oxygen vacancy doping gradient introduced during thermal oxidation. These vacancies are mobile within the tin oxide host structure and their injection from the ATO segment into the undoped tin oxide segment results in altered conductivity of the device, when an external bias is applied.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Leibniz-University Hannover
Institute of Inorganic Chemistry
30167 Hannover
Germany
University of Konstanz
Department of Chemistry
Universitätsstrasse 10
78457 Konstanz
Funding
Deutsche Forschungsgemeinschaft · Universität Konstanz
References (41)
Show source-provided references
1. Chua · IEEE Trans. Circuit Theory · 1971 · CT18 · 50710.1109/TCT.1971.1083337 ↗
2. Strukov · Nature · 2008 · 453 · 8010.1038/nature06932 ↗
3. Mohammad · Nanotechnol. Rev. · 2016 · 5 · 31110.1515/ntrev-2015-0029 ↗
4. Lee · Adv. Mater. · 2018 · 30 · 170277010.1002/adma.201702770 ↗
5. Zidan · Nat. Electron. · 2018 · 1 · 2210.1038/s41928-017-0006-8 ↗
6. Wong · Proc. IEEE · 2012 · 100 · 195110.1109/JPROC.2012.2190369 ↗
7. Jo · Nano Lett. · 2010 · 10 · 129710.1021/nl904092h ↗
8. Thomas · J. Phys. D: Appl. Phys. · 2013 · 46 · 12
9. Bendova · Adv. Mater. Interfaces · 2016 · 3 · 160051210.1002/admi.201600512 ↗
10. C.-T. Sah , Fundamentals of solid state electronics , World Scientific Publishing Company , 199110.1142/1388 ↗
11. Yang · Nat. Nanotechnol. · 2008 · 3 · 42910.1038/nnano.2008.160 ↗
12. Tahar · J. Appl. Phys. · 1998 · 83 · 263110.1063/1.367025 ↗
13. Granqvist · Sol. Energy Mater. Sol. Cells · 2007 · 91 · 152910.1016/j.solmat.2007.04.031 ↗
14. Das · Prog. Mater. Sci. · 2014 · 66 · 11210.1016/j.pmatsci.2014.06.003 ↗
15. Han · Small · 2018 · 14 · 170386010.1002/smll.201703860 ↗
16. Sun · ACS Appl. Mater. Interfaces · 2014 · 6 · 549410.1021/am405628r ↗
17. Simanjuntak · Nanoscale Res. Lett. · 2016 · 11 · 3110.1186/s11671-016-1570-y ↗
18. Wang · Adv. Mater. · 2016 · 28 · 383110.1002/adma.201503080 ↗
19. Huang · NPG Asia Mater. · 2014 · 6 · 8510.1038/am.2013.81 ↗
20. Lee · Chem. Rev. · 2014 · 114 · 748710.1021/cr500002z ↗
21. Serra · Appl. Mater. Today · 2018 · 12 · 20710.1016/j.apmt.2018.05.006 ↗
22. Mieszawska · Small · 2007 · 3 · 72210.1002/smll.200600727 ↗
23. Lai · Chem. Mater. · 2006 · 18 · 223310.1021/cm051613j ↗
24. Wang · Sci. Rep. · 2013 · 3 · 8
25. Jiao · Acta Phys.-Chim. Sin. · 2012 · 28 · 243610.3866/PKU.WHXB201209145 ↗
26. Shi · Vacuum · 2015 · 117 · 5010.1016/j.vacuum.2015.04.011 ↗
27. Masuda · Science · 1995 · 268 · 146610.1126/science.268.5216.1466 ↗
28. Masuda · Appl. Phys. Lett. · 1997 · 71 · 277010.1063/1.120128 ↗
29. Shin · Curr. Appl. Phys. · 2016 · 16 · 20710.1016/j.cap.2015.11.017 ↗
30. Galdun · ACS Appl. Nano Mater. · 2018 · 1 · 706610.1021/acsanm.8b01836 ↗
31. Ponja · J. Mater. Chem. C · 2018 · 6 · 725710.1039/C8TC01929K ↗
32. Nieh · Appl. Phys. Lett. · 2014 · 104 · 21350110.1063/1.4880210 ↗
33. Zeng · ACS Sens. · 2017 · 2 · 185410.1021/acssensors.7b00716 ↗
34. Kolmakov · Adv. Mater. · 2003 · 15 · 99710.1002/adma.200304889 ↗
35. Kim · J. Alloys Compd. · 2016 · 681 · 7510.1016/j.jallcom.2016.04.215 ↗
36. Le · Nanotechnology · 2019 · 30 · 32520110.1088/1361-6528/ab19c9 ↗
37. Milano · Adv. Electron. Mater. · 2019 · 5 · 180090910.1002/aelm.201800909 ↗
38. Nagashima · Nano Lett. · 2010 · 10 · 135910.1021/nl9042906 ↗
39. Flowers · Nanoscale Horiz. · 2016 · 1 · 31310.1039/C6NH00020G ↗
40. Valov · Nanotechnology · 2011 · 22 · 25400310.1088/0957-4484/22/25/254003 ↗
41. Yuan · Curr. Appl. Phys. · 2020 · 20 · 43110.1016/j.cap.2020.01.005 ↗