journal article·2024-05-28

A Self-Selector and Self-Rectifying Charge-Trap-Based Resistive Switching Device Using In2Se3 Thin Films

Chanchal, Kajal Jindal, Monika Tomar, Pradip K. Jha

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Abstract

Indium selenide (In 2 Se 3 ) thin films have been observed to be a promising candidate for resistive switching-based neuromorphic computing applications owing to their various ferroelectric phases. It has been observed that the defects play a major role in the phase formation and performance of In 2 Se 3 -based devices. In the present work, a forming-free, nonlinear, and self-rectifying charge-trap-based resistive switching device is demonstrated in planar and vertical geometry using the layered phases of In 2 Se 3 layers, namely, α-In 2 Se 3 and β-In 2 Se 3 . The formation energy of intrinsic point defect calculations carried out using density functional theory (DFT) reveals that the deep trap levels play a major role in charge-trap-assisted resistive switching, exhibiting a flat band potential, which depends on the applied voltage range with an on/off ratio of ∼10 2 for α-In 2 Se 3 in the planar configuration. Further, α-In 2 Se 3 is utilized for the thickness-dependent planar resistive switching, where bilayer α-In 2 Se 3 shows a high rectification ratio of ∼10 2 . On the other hand, bilayer α-In 2 Se 3 is exploited for out-of-plane switching characteristics and it is observed that bilayer α-In 2 Se 3 behaves as a self-selector device with a selectivity of ∼10 5 . Thus, the present study enlightens the charge-trap-assisted resistive switching in In 2 Se 3 layers due to the presence of intrinsic point defects. Also, the bilayer α-In 2 Se 3 exhibits a planar self-rectifying and vertical self-selector memory function for future application in crossbar array-based neuromorphic computing devices offering lower sneak path currents and power consumption.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Perovskite Materials and Applications · 2D Materials and Applications

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

Affiliations

Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India

Department of Physics, Kirori Mal College, University of Delhi, Delhi 110007, India

Department of Physics, Miranda House, University of Delhi, Delhi 110007, India

Department of Physics, Deen Dayal Upadhyaya College, University of Delhi, Delhi 110078, India

References (42)

Show source-provided references
1. 10.1002/aelm.20210060910.1002/aelm.202100609 ↗
2. 10.1088/1674-4926/42/1/01310110.1088/1674-4926/42/1/013101 ↗
3. 10.1038/s41427-018-0101-y10.1038/s41427-018-0101-y ↗
4. 10.1038/s41699-022-00309-510.1038/s41699-022-00309-5 ↗
5. 10.1002/aelm.20190081810.1002/aelm.201900818 ↗
6. 10.1002/adma.20170356810.1002/adma.201703568 ↗
7. 10.1021/acs.nanolett.7b0485210.1021/acs.nanolett.7b04852 ↗
8. 10.1021/acs.nanolett.8b0268810.1021/acs.nanolett.8b02688 ↗
9. 10.1038/s41467-021-27617-610.1038/s41467-021-27617-6 ↗
10. 10.1002/adfm.20190056610.1002/adfm.201900566 ↗
11. 10.1002/afmd.20200460910.1002/afmd.202004609 ↗
12. 10.1002/adma.20200870910.1002/adma.202008709 ↗
13. 10.1039/c7ra08438b10.1039/c7ra08438b ↗
14. 10.1063/1.363322710.1063/1.3633227 ↗
15. 10.1016/j.ceramint.2017.05.28910.1016/j.ceramint.2017.05.289 ↗
16. 10.1016/j.matchar.2021.11157810.1016/j.matchar.2021.111578 ↗
17. 10.1002/advs.20220565410.1002/advs.202205654 ↗
18. 10.1021/acs.nanolett.6b0178110.1021/acs.nanolett.6b01781 ↗
19. 10.1016/j.apsusc.2022.15350510.1016/j.apsusc.2022.153505 ↗
20. 10.1103/PhysRevLett.80.89110.1103/PhysRevLett.80.891 ↗
21. 10.1063/1.502037610.1063/1.5020376 ↗
22. 10.1021/acs.inorgchem.8b0077810.1021/acs.inorgchem.8b00778 ↗
23. 10.1021/acs.nanolett.5b0219010.1021/acs.nanolett.5b02190 ↗
24. 10.1039/C7NR01840A10.1039/C7NR01840A ↗
25. 10.1016/j.ceramint.2022.04.00710.1016/j.ceramint.2022.04.007 ↗
26. 10.1021/acs.nanolett.5b0357510.1021/acs.nanolett.5b03575 ↗
27. 10.1088/1674-1056/27/3/03780210.1088/1674-1056/27/3/037802 ↗
28. 10.1021/acs.chemmater.9b0349910.1021/acs.chemmater.9b03499 ↗
29. 10.1002/adom.20160036510.1002/adom.201600365 ↗
30. 10.1021/acs.nanolett.5b0159010.1021/acs.nanolett.5b01590 ↗
31. 10.1016/j.apmt.2020.10073410.1016/j.apmt.2020.100734 ↗
32. 10.1063/1.165602210.1063/1.1656022 ↗
33. 10.1007/s10008-021-05054-110.1007/s10008-021-05054-1 ↗
34. 10.1021/acsanm.0c0184410.1021/acsanm.0c01844 ↗
35. 10.1515/nanoph-2019-045010.1515/nanoph-2019-0450 ↗
36. 10.1016/j.apcatb.2019.11821810.1016/j.apcatb.2019.118218 ↗
37. 10.1039/C8RA05677C10.1039/C8RA05677C ↗
38. 10.1088/2053-1583/aac47910.1088/2053-1583/aac479 ↗
39. 10.1038/s41467-019-11187-910.1038/s41467-019-11187-9 ↗
40. 10.1002/adfm.20200999910.1002/adfm.202009999 ↗
41. Midya, R.; Wang, Z.; Zhang, J.Anatomy of Ag/Hafnia-Based Selectors with 1010 Nonlinearity Adv. Mater. 2017; Vol. 29 1210.1002/adma.201604457.10.1002/adma.201604457 ↗
42. 10.1002/advs.20230303210.1002/advs.202303032 ↗