journal article·2020-09-22

Nonvolatile and Neuromorphic Memory Devices Using Interfacial Traps in Two-Dimensional WSe2/MoTe2 Stack Channel

Sam Park, Yeonsu Jeong, Hye-Jin Jin, Junkyu Park, Hyenam Jang, Sol Lee, Woong Huh, Hyunmin Cho, Hyung Gon Shin, Kwanpyo Kim, Chul-Ho Lee, Shinhyun Choi, Seongil Im

ACS Nano· Volume 14· Issue 9
Publisher page

Abstract

Very recently, stacked two-dimensional materials have been studied, focusing on the van der Waals interaction at their stack junction interface. Here, we report field effect transistors (FETs) with stacked transition metal dichalcogenide (TMD) channels, where the heterojunction interface between two TMDs appears useful for nonvolatile or neuromorphic memory FETs. A few nanometer-thin WSe2 and MoTe2 flakes are vertically stacked on the gate dielectric, and bottom p-MoTe2 performs as a channel for hole transport. Interestingly, the WSe2/MoTe2 stack interface functions as a hole trapping site where traps behave in a nonvolatile manner, although trapping/detrapping can be controlled by gate voltage (VGS). Memory retention after high VGS pulse appears longer than 10000 s, and the Program/Erase ratio in a drain current is higher than 200. Moreover, the traps are delicately controllable even with small VGS, which indicates that a neuromorphic memory is also possible with our heterojunction stack FETs. Our stack channel FET demonstrates neuromorphic memory behavior of ∼94% recognition accuracy.
Source: openalex_inverted_index · Quality: baseline

Research context

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

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

Affiliations

Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea

The school of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea

Funding

National Research Foundation of Korea · Korea Evaluation Institute of Industrial Technology · KU-KIST Graduate School of Converging Science and Technology, Korea University

References (40)

Show source-provided references
1. 10.1038/nnano.2012.19310.1038/nnano.2012.193 ↗
2. 10.1021/nn500064s10.1021/nn500064s ↗
3. 10.1002/adma.20180447010.1002/adma.201804470 ↗
4. 10.1038/nnano.2010.27910.1038/nnano.2010.279 ↗
5. 10.1002/adfm.20170382210.1002/adfm.201703822 ↗
6. 10.1021/nn305275h10.1021/nn305275h ↗
7. 10.1002/adma.20180144710.1002/adma.201801447 ↗
8. 10.1002/adma.20130584510.1002/adma.201305845 ↗
9. 10.1002/smll.20170093310.1002/smll.201700933 ↗
10. 10.1021/acsami.9b2228810.1021/acsami.9b22288 ↗
11. 10.1002/adma.20170179810.1002/adma.201701798 ↗
12. 10.1002/adma.20150477910.1002/adma.201504779 ↗
13. 10.1002/adfm.20190821010.1002/adfm.201908210 ↗
14. 10.1002/adfm.20160211310.1002/adfm.201602113 ↗
15. 10.1038/nature1538710.1038/nature15387 ↗
16. 10.1038/s41467-020-15419-110.1038/s41467-020-15419-1 ↗
17. 10.1021/acs.nanolett.7b0533810.1021/acs.nanolett.7b05338 ↗
18. 10.1038/s41699-018-0082-210.1038/s41699-018-0082-2 ↗
19. 10.1002/adma.20180707510.1002/adma.201807075 ↗
20. 10.1038/ncomms596610.1038/ncomms5966 ↗
21. 10.1126/science.aav191010.1126/science.aav1910 ↗
22. 10.1038/s41567-018-0123-y10.1038/s41567-018-0123-y ↗
23. 10.1021/acs.nanolett.7b0074810.1021/acs.nanolett.7b00748 ↗
24. 10.1038/s41467-019-12035-610.1038/s41467-019-12035-6 ↗
25. 10.1038/s41467-019-11381-910.1038/s41467-019-11381-9 ↗
26. 10.1038/s41565-020-0647-z10.1038/s41565-020-0647-z ↗
27. 10.1038/nnano.2015.5610.1038/nnano.2015.56 ↗
28. 10.1364/OE.21.00490810.1364/OE.21.004908 ↗
29. 10.1021/nl502557g10.1021/nl502557g ↗
30. 10.1039/C7NR03445H10.1039/C7NR03445H ↗
31. 10.1016/j.apsusc.2016.10.05010.1016/j.apsusc.2016.10.050 ↗
32. 10.1021/acsnano.5b0251110.1021/acsnano.5b02511 ↗
33. 10.1002/admi.20180081210.1002/admi.201800812 ↗
34. 10.1016/j.nanoen.2019.06.02910.1016/j.nanoen.2019.06.029 ↗
35. 10.1063/1.477409010.1063/1.4774090 ↗
36. 10.1002/adfm.20180120410.1002/adfm.201801204 ↗
37. 10.1038/ncomms1589110.1038/ncomms15891 ↗
38. 10.1021/acsnano.7b0011310.1021/acsnano.7b00113 ↗
39. Chen, P.Y.; Peng, X.; Yu, S. NeuroSim+: An Integrated Device-To-Algorithm Framework for Benchmarking Synaptic Devices and Array Architectures. 2017 IEEE International Electron Devices Meeting (IEDM), San Francisco, Dec 2–6, 2017; IEEE, 2017; pp 6.1.1–6.1.4.10.1109/IEDM.2017.8268337 ↗
40. 10.1038/s41563-017-0001-510.1038/s41563-017-0001-5 ↗