journal article·2024Open access

Conductive filament distribution in nano-scale electrochemical metallization cells

Maximilian Speckbacher, Michael Rinderle, Oliver Bienek, Ian D. Sharp, Alessio Gagliardi, Marc Tornow

Nanoscale· Volume 16· Issue 42

Abstract

We report a combined experimental and theoretical study of the spatial distributions and sizes of conductive filaments in nano-scale electrochemical metallization (ECM) cells. Each cell comprises a silver nanocube as active electrode, a titanium dioxide (TiO2) or aluminum oxide (Al2O3) layer as dielectric, and a highly-doped silicon substrate as passive counter electrode. Following electroforming of the ECM cell and subsequent mechanical delamination of the silver nanocubes, current maps at previous particle locations reveal an intriguing metal distribution in the TiO2, with preferential accumulation close to the original locations of the nanocube edges. We assign this behavior to electric field enhancements close to the cube edge positions. In contrast, filaments in Al2O3 layers show a comparatively homogenous distribution, which may be assigned to its lower dielectric permittivity. By increasing the oxide thickness, the total area of conductive spots in the current maps increases monotonically for both materials. Kinetic Monte-Carlo simulations of ion migration dynamics in TiO2 confirm the experimental observations, describing both the preferred locations and oxide thickness-dependent metal loadings associated with filament formation. Overall, our findings are highly valuable for the design of future electrochemical metallization cells, especially in the sub-100 nm regime, where optimal filament control is of major importance for achieving lowest device-to-device variability.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Conducting polymers and applications

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

Affiliations

Molecular Electronics, Department of Electrical Engineering, TUM School of Computation, Information and Technology, Technical University of Munich, 85748 Garching, Germany

Chair of Simulation of Nanosystems for Energy Conversion, Department of Electrical Engineering, TUM School of Computation, Information and Technology, Atomistic Modeling Center (AMC), Munich Data Science Institute (MDSI), Technical University of Munich, 85748 Garching, Germany

Walter Schottky Institute, Technical University of Munich, 85748 Garching, Germany

Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany

Fraunhofer Institute for Electronic Microsystems and Solid State Technologies (EMFT), 80686 Munich, Germany

Funding

Deutsche Forschungsgemeinschaft

References (36)

Show source-provided references
1. Lee · Nano Res. · 2020 · 13 · 122810.1007/s12274-020-2616-0 ↗
2. Zhu · Appl. Phys. Rev. · 2020 · 7 · 01131210.1063/1.5118217 ↗
3. Wang · Nat. Rev. Mater. · 2020 · 5 · 17310.1038/s41578-019-0159-3 ↗
4. Sun · Adv. Funct. Mater. · 2021 · 31 · 200677310.1002/adfm.202006773 ↗
5. Yang · Nat. Nanotechnol. · 2013 · 8 · 1310.1038/nnano.2012.240 ↗
6. Valov · Nanotechnology · 2011 · 22 · 25400310.1088/0957-4484/22/25/254003 ↗
7. Waser · Adv. Mater. · 2009 · 21 · 263210.1002/adma.200900375 ↗
8. Chen · Adv. Mater. · 2023 · 35 · 230192410.1002/adma.202301924 ↗
9. Pan · Mater. Sci. Eng., R · 2014 · 83 · 110.1016/j.mser.2014.06.002 ↗
10. Lanza · Adv. Mater. · 2023 · 35 · 220540210.1002/adma.202205402 ↗
11. Sangwan · Nat. Nanotechnol. · 2020 · 15 · 51710.1038/s41565-020-0647-z ↗
12. Ielmini · Semicond. Sci. Technol. · 2016 · 31 · 06300210.1088/0268-1242/31/6/063002 ↗
13. Yang · Nat. Commun. · 2012 · 3 · 73210.1038/ncomms1737 ↗
14. Menzel · Adv. Funct. Mater. · 2015 · 25 · 630610.1002/adfm.201500825 ↗
15. Lanza · Materials · 2014 · 7 · 215510.3390/ma7032155 ↗
16. Celano · Nano Lett. · 2014 · 14 · 240110.1021/nl500049g ↗
17. Yang · J. Electroceram. · 2017 · 39 · 7310.1007/s10832-017-0069-y ↗
18. Speckbacher · ACS Appl. Electron. Mater. · 2020 · 2 · 109910.1021/acsaelm.0c00099 ↗
19. Kim · Appl. Microsc. · 2021 · 51 · 710.1186/s42649-021-00056-9 ↗
20. Song · Appl. Phys. Lett. · 2021 · 118 · 08050110.1063/5.0040984 ↗
21. Li · J. Mater. Chem. C · 2020 · 8 · 1629510.1039/d0tc03639k ↗
22. You · ACS Nano · 2016 · 10 · 947810.1021/acsnano.6b04578 ↗
23. Shin · Adv. Electron. Mater. · 2016 · 2 · 160023310.1002/aelm.201600233 ↗
24. Spring · Small · 2020 · 16 · 200322410.1002/smll.202003224 ↗
25. Cheng · Commun. Phys. · 2019 · 2 · 2810.1038/s42005-019-0125-9 ↗
26. Li · J. Alloys Compd. · 2022 · 890 · 16181410.1016/j.jallcom.2021.161814 ↗
27. Liu · Adv. Mater. · 2022 · 34 · 220320910.1002/adma.202203209 ↗
28. Sun · ACS Appl. Mater. Interfaces · 2017 · 9 · 3406410.1021/acsami.7b09710 ↗
29. Tao · Appl. Surf. Sci. · 2021 · 539 · 14816110.1016/j.apsusc.2020.148161 ↗
30. Zhang · ACS Appl. Mater. Interfaces · 2023 · 15 · 2121910.1021/acsami.3c00371 ↗
31. Seok · Adv. Funct. Mater. · 2014 · 24 · 531610.1002/adfm.201303520 ↗
32. Speckbacher · Adv. Electron. Mater. · 2019 · 5 · 190009810.1002/aelm.201900098 ↗
33. Wang · Adv. Funct. Mater. · 2018 · 28 · 170486210.1002/adfm.201704862 ↗
34. Ielmini · J. Comput. Electron. · 2017 · 16 · 112110.1007/s10825-017-1101-9 ↗
35. Sun · Nat. Commun. · 2019 · 10 · 345310.1038/s41467-019-11411-6 ↗
36. Liu · IEEE J. Electron Devices Soc. · 2020 · 8 · 98110.1109/JEDS.2020.3023015 ↗