journal article·2021-09-28Open access

Comprehensive Model of Electron Conduction in Oxide-Based Memristive Devices

Carsten Funck, Stephan Menzel

Abstract

Memristive devices are two-terminal devices that can change their resistance state upon application of appropriate voltage stimuli.The resistance can be tuned over a wide resistance range enabling applications such as multibit data storage or analog computing-in-memory concepts.One of the most promising classes of memristive devices is based on the valence change mechanism in oxide-based devices.In these devices, a configurational change of oxygen defects, i.e. oxygen vacancies, leads to the change of the device resistance.A microscopic understanding of the conduction is necessary in order to design memristive devices with specific resistance properties.In this paper, we discuss the conduction mechanism proposed in the literature and propose a comprehensive, microscopic model of the conduction mechanism in this class of devices.To develop this microscopic picture of the conduction, ab initio simulation models are developed.These simulations suggest two different types of conduction, which are both limited by a tunneling through the Schottky barrier at the metal electrode contact.The difference between the two conduction mechanisms is the following: for the first type, the electrons tunnel into the conduction band and, in the second type, into the vacancy defect states.These two types of conduction differ in their current voltage relation, which has been detected experimentally.The origin of the resistive switching is identical for the two types of conduction and is based on a modification of the tunneling distance due to the oxygen vacancy induced screening of the Schottky barrier.This understanding may help to design optimized devices in terms of the dynamic resistance range for specific applications.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Ferroelectric and Negative Capacitance Devices

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

Affiliations

Institut für Werkstoffe der Elektrotechnik 2, RWTH Aachen University, 52074 Aachen, Germany

Peter Grünberg Institut (PGI-7), Forschungszentrum Juelich GmbH, 52425 Juelich, Germany

Funding

Bundesministerium f??r Bildung und Forschung · Deutsche Forschungsgemeinschaft

References (100+)

Show source-provided references
1. 10.1002/adma.20090037510.1002/adma.200900375 ↗
2. 10.1557/mrs.2011.35610.1557/mrs.2011.356 ↗
3. 10.1088/1361-6528/ab208410.1088/1361-6528/ab2084 ↗
4. 10.1088/0957-4484/22/25/25400210.1088/0957-4484/22/25/254002 ↗
5. 10.1038/nmat202310.1038/nmat2023 ↗
6. 10.1038/s41565-020-0655-z10.1038/s41565-020-0655-z ↗
7. 10.1038/s41578-019-0159-310.1038/s41578-019-0159-3 ↗
8. 10.1063/1.511821710.1063/1.5118217 ↗
9. 10.1038/s41563-019-0291-x10.1038/s41563-019-0291-x ↗
10. 10.1007/s10853-018-2134-610.1007/s10853-018-2134-6 ↗
11. 10.1038/s41928-018-0092-210.1038/s41928-018-0092-2 ↗
12. 10.1080/23746149.2016.125958510.1080/23746149.2016.1259585 ↗
13. Hu, M.; Strachan, J. P.; Li, Z.; Merced Grafals, E.; Davila, N.; Graves, C.; Lam, S.; Ge, N.; Williams, R. S.; Yang, J. Dot-Product Engine for Neuromorphic Computing: Programming 1T1M Crossbar to Accelerate Matrix-Vector Multiplication.53rd Design Automation Conference, 2016.10.1145/2897937.2898010 ↗
14. 10.1109/ASPDAC.2015.705898910.1109/ASPDAC.2015.7058989 ↗
15. 10.1038/s41928-018-0054-810.1038/s41928-018-0054-8 ↗
16. 10.1109/TED.2018.286535210.1109/TED.2018.2865352 ↗
17. 10.1038/s42256-018-0001-410.1038/s42256-018-0001-4 ↗
18. 10.1038/s42256-019-0089-110.1038/s42256-019-0089-1 ↗
19. 10.1038/s41928-019-0221-610.1038/s41928-019-0221-6 ↗
20. 10.3389/fnins.2021.58090910.3389/fnins.2021.580909 ↗
21. 10.1038/s41598-021-83382-y10.1038/s41598-021-83382-y ↗
22. 10.1109/TED.2020.300124710.1109/TED.2020.3001247 ↗
23. 10.1002/aisy.20200014110.1002/aisy.202000141 ↗
24. 10.1002/aisy.20190015610.1002/aisy.201900156 ↗
25. 10.1109/TCSI.2020.300046810.1109/TCSI.2020.3000468 ↗
26. 10.1002/aisy.20190008210.1002/aisy.201900082 ↗
27. 10.1002/aelm.20200067210.1002/aelm.202000672 ↗
28. 10.1109/JEDS.2020.298740210.1109/JEDS.2020.2987402 ↗
29. 10.1109/TCSI.2019.290247510.1109/TCSI.2019.2902475 ↗
30. 10.1038/s41598-019-51039-610.1038/s41598-019-51039-6 ↗
31. 10.1140/epjst/e2019-900042-x10.1140/epjst/e2019-900042-x ↗
32. 10.1038/s41598-017-18329-310.1038/s41598-017-18329-3 ↗
33. 10.1002/adfm.20170472510.1002/adfm.201704725 ↗
34. 10.1002/aelm.20180018910.1002/aelm.201800189 ↗
35. 10.1186/s11671-016-1807-910.1186/s11671-016-1807-9 ↗
36. 10.1109/TNANO.2017.269171310.1109/TNANO.2017.2691713 ↗
37. 10.1109/LED.2016.264594610.1109/LED.2016.2645946 ↗
38. 10.1007/s12274-016-1260-110.1007/s12274-016-1260-1 ↗
39. 10.1038/srep2396710.1038/srep23967 ↗
40. Gaillardon, P.E.; Amaru, L.; Siemon, A.; Linn, E.; Waser, R.; Chattopadhyay, A.; De Micheli, G. The Programmable Logic-in-Memory (PLiM) Computer. In2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, March 14–18, 2016; pp 1–6.10.3850/9783981537079_0970 ↗
41. 10.1038/srep3665210.1038/srep36652 ↗
42. 10.1109/TNANO.2016.257024810.1109/TNANO.2016.2570248 ↗
43. 10.1109/MCAS.2016.258367310.1109/MCAS.2016.2583673 ↗
44. 10.1002/aelm.20150013810.1002/aelm.201500138 ↗
45. 10.1109/JETCAS.2015.239821710.1109/JETCAS.2015.2398217 ↗
46. 10.1002/adfm.20150086510.1002/adfm.201500865 ↗
47. 10.1109/TCSII.2014.235729210.1109/TCSII.2014.2357292 ↗
48. 10.1109/LED.2011.212743910.1109/LED.2011.2127439 ↗
49. 10.1016/j.mee.2010.06.04210.1016/j.mee.2010.06.042 ↗
50. 10.1038/nmat125610.1038/nmat1256 ↗
51. 10.1021/acsami.5b0611710.1021/acsami.5b06117 ↗
52. 10.1038/nature0894010.1038/nature08940 ↗
53. 10.3389/fnins.2021.66185610.3389/fnins.2021.661856 ↗
54. 10.1038/s41467-018-04933-y10.1038/s41467-018-04933-y ↗
55. 10.1039/C6NR03810G10.1039/C6NR03810G ↗
56. Maier J. · 200410.1002/0470020229 ↗
57. 10.1016/S0081-1947(08)60135-610.1016/S0081-1947(08)60135-6 ↗
58. 10.1088/0957-4484/20/21/21520110.1088/0957-4484/20/21/215201 ↗
59. Marchewka, A.; Waser, R.; Menzel, S. Physical Modeling of the Electroforming Process in Resistive-Switching Devices (talk). In2017 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), September 7–9, Kamakura, Japan, 2017.10.23919/SISPAD.2017.8085282 ↗
60. 10.1109/TNANO.2018.286790410.1109/TNANO.2018.2867904 ↗
61. 10.1109/TED.2012.220232010.1109/TED.2012.2202320 ↗
62. 10.1109/IMW.2012.621364810.1109/IMW.2012.6213648 ↗
63. 10.1038/nmat161410.1038/nmat1614 ↗
64. 10.1002/smll.20080132310.1002/smll.200801323 ↗
65. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
66. 10.1063/1.326462110.1063/1.3264621 ↗
67. 10.1002/adma.20170021210.1002/adma.201700212 ↗
68. 10.1021/acsami.8b0906810.1021/acsami.8b09068 ↗
69. 10.1038/nmat307010.1038/nmat3070 ↗
70. 10.1109/IMW.2013.658209510.1109/IMW.2013.6582095 ↗
71. 10.1109/TED.2021.304976510.1109/TED.2021.3049765 ↗
72. 10.1109/ACCESS.2021.306343710.1109/ACCESS.2021.3063437 ↗
73. 10.1039/D0MA00488J10.1039/D0MA00488J ↗
74. 10.1116/1.494012910.1116/1.4940129 ↗
75. 10.1021/acsaelm.8b0006410.1021/acsaelm.8b00064 ↗
76. 10.1021/acs.nanolett.8b0302310.1021/acs.nanolett.8b03023 ↗
77. 10.1038/srep1276610.1038/srep12766 ↗
78. 10.1186/s11671-016-1602-710.1186/s11671-016-1602-7 ↗
79. Liu, X.; Nandi, S. K.; Venkatachalam, D. K.; Li, S.; Belay, K.; Elliman, R. G. Finite Element Modeling of Resistive Switching in Nb2O5-based Memory Device. InConference on Optoelectronic and Microelectronic Materials and Devices (COMMAD), Univ Western Australia, Perth, Australia, 2014; pp 280–282.10.1109/COMMAD.2014.7038711 ↗
80. 10.1038/srep1596510.1038/srep15965 ↗
81. 10.1038/s41598-020-68211-y10.1038/s41598-020-68211-y ↗
82. 10.1063/1.494350810.1063/1.4943508 ↗
83. 10.1088/1361-6463/aa64bc10.1088/1361-6463/aa64bc ↗
84. 10.1007/s00339-019-2659-910.1007/s00339-019-2659-9 ↗
85. 10.1063/1.489640210.1063/1.4896402 ↗
86. 10.1063/1.497453510.1063/1.4974535 ↗
87. 10.1063/1.510865410.1063/1.5108654 ↗
88. 10.1109/TED.2018.284987210.1109/TED.2018.2849872 ↗
89. 10.1016/j.apsusc.2015.05.17610.1016/j.apsusc.2015.05.176 ↗
90. 10.1002/aelm.20180040710.1002/aelm.201800407 ↗
91. 10.1063/1.487823610.1063/1.4878236 ↗
92. 10.1039/C5RA02605A10.1039/C5RA02605A ↗
93. 10.1063/1.496052510.1063/1.4960525 ↗
94. 10.1116/1.486391510.1116/1.4863915 ↗
95. 10.1063/1.488021010.1063/1.4880210 ↗
96. 10.1109/LED.2016.254287910.1109/LED.2016.2542879 ↗
97. 10.1186/1556-276X-9-310.1186/1556-276X-9-3 ↗
98. 10.1063/1.336928510.1063/1.3369285 ↗
99. 10.1021/acsami.7b1958610.1021/acsami.7b19586 ↗
100. 10.1103/PhysRevB.102.03530710.1103/PhysRevB.102.035307 ↗