journal article·2024-02-27

Memristive Dendritic Device with a Chemical Vapor-Deposited Monolayer MoS2 Film

Asmita S. Thool, Sourodeep Roy, Upasana Mukherjee, Prahalad Kanti Barman, Saroj Poudyal, Abhishek Misra, Bhaswar Chakrabarti

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Abstract

The discovery of graphene in the early 2000s led to the rapid development of an emerging class of material system, now widely known as two-dimensional (2D) materials. The atomic-scale thickness of 2D materials leads to rich transport physics beneficial for many electronic/optoelectronic devices. 2D transition-metal dichalcogenides (2D-TMDCs) have been widely investigated in logic applications over the past decade. However, out-of-plane transport in ultrathin 2D-TMDCs and the resulting applications in memristive devices remain relatively less explored. Challenges remain in large-area growth, as well as in the fundamental understanding of resistance switching in 2D-TMDC films. In this work, we report a systematic investigation of resistive switching in monolayer (1L) MoS 2 films. We demonstrate large-area growth of chemical vapor-deposited (CVD) 1L-MoS 2 films. Two-terminal cross-point devices are fabricated with 1L MoS 2 as the active material. Different switching mechanisms emerge in the fabricated Au/Ti/1L-MoS 2 /Au/Cr devices depending on the operating conditions. We observe a partially volatile mode of operation that can be used to mimic the dynamics of a dendritic junction in a biological neuron. While dendritic junctions are generally ignored in machine learning, they play a crucial role in biological networks through signal filtering and integration. This, in turn, leads to enhanced energy efficiency. By demonstrating MoS 2 -based dendritic memristive devices, we envisage the development of more efficient and bioinspired neural networks.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · 2D Materials and Applications · Advanced biosensing and bioanalysis techniques

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

Affiliations

Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India

Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India

Funding

Science and Engineering Research Board

References (44)

Show source-provided references
1. 10.1073/pnas.050284810210.1073/pnas.0502848102 ↗
2. 10.1038/s41699-020-00162-410.1038/s41699-020-00162-4 ↗
3. Madkour, L. H. Nanoelectronic Materials: Fundamentals and Applications; Springer International Publishing: Cham, 2019, pp 165–245.10.1007/978-3-030-21621-4_7 ↗
4. Kolobov, A. V.; Tominaga, J. Two-Dimensional Transition-Metal Dichalcogenides; Springer International Publishing: Cham, 2016, pp 473–512.10.1007/978-3-319-31450-1_14 ↗
5. 10.1016/j.isci.2021.10349110.1016/j.isci.2021.103491 ↗
6. Krishnaprasad Sharada, A. P. MoS2 Based Memristive Synapses for Neuromorphic Computing, University of Central Florida, 2022.
7. Dev, D. Study of MoS2/high-K Interface and Implementation of MoS2 Based Memristor for Neuromorphic Computing Applications, University of Central Florida, 2021.
8. 10.1021/acsami.5b0602710.1021/acsami.5b06027 ↗
9. 10.1021/acs.nanolett.0c0465710.1021/acs.nanolett.0c04657 ↗
10. 10.1016/j.mee.2023.11196310.1016/j.mee.2023.111963 ↗
11. 10.1021/acs.nanolett.2c0500710.1021/acs.nanolett.2c05007 ↗
12. 10.1002/aelm.20210116110.1002/aelm.202101161 ↗
13. 10.1021/acsaelm.9b0065510.1021/acsaelm.9b00655 ↗
14. 10.1021/acs.nanolett.7b0434210.1021/acs.nanolett.7b04342 ↗
15. 10.1021/acs.nanolett.5b0426010.1021/acs.nanolett.5b04260 ↗
16. 10.1002/smll.20190142310.1002/smll.201901423 ↗
17. 10.1021/acs.nanolett.8b0514010.1021/acs.nanolett.8b05140 ↗
18. 10.1016/j.isci.2020.10167610.1016/j.isci.2020.101676 ↗
19. 10.1109/LED.2020.298824710.1109/LED.2020.2988247 ↗
20. 10.1016/j.polymer.2021.12407610.1016/j.polymer.2021.124076 ↗
21. 10.1002/aelm.20180086610.1002/aelm.201800866 ↗
22. 10.1039/C9TC03842F10.1039/C9TC03842F ↗
23. 10.1038/s41467-022-35160-110.1038/s41467-022-35160-1 ↗
24. 10.1002/adma.20220494410.1002/adma.202204944 ↗
25. 10.1038/s41563-017-0001-510.1038/s41563-017-0001-5 ↗
26. 10.1021/acsnano.0c0320110.1021/acsnano.0c03201 ↗
27. 10.1038/s41699-023-00388-y10.1038/s41699-023-00388-y ↗
28. 10.1155/2014/57816810.1155/2014/578168 ↗
29. Sejnowski T. J. · 1989 · 9410.1016/B978-0-12-148955-7.50010-2 ↗
30. 10.1038/s41565-020-0722-510.1038/s41565-020-0722-5 ↗
31. 10.1038/3504455210.1038/35044552 ↗
32. 10.1038/915810.1038/9158 ↗
33. 10.1146/annurev.neuro.28.061604.13570310.1146/annurev.neuro.28.061604.135703 ↗
34. 10.1088/0957-4484/27/16/16520310.1088/0957-4484/27/16/165203 ↗
35. 10.1038/s41467-020-14753-810.1038/s41467-020-14753-8 ↗
36. 10.1103/PhysRevB.91.19541110.1103/PhysRevB.91.195411 ↗
37. 10.1039/D0RA03372C10.1039/D0RA03372C ↗
38. 10.1021/nl903868w10.1021/nl903868w ↗
39. 10.1103/PhysRevLett.105.13680510.1103/PhysRevLett.105.136805 ↗
40. 10.1063/1.505369910.1063/1.5053699 ↗
41. 10.1021/acsnano.7b0381910.1021/acsnano.7b03819 ↗
42. 10.1038/srep0186610.1038/srep01866 ↗
43. 10.1103/PhysRevB.87.08130710.1103/PhysRevB.87.081307 ↗
44. 10.1038/srep0564910.1038/srep05649 ↗