Phonon-Assisted Charge Trapping and Threshold Voltage Modulation in MoS2 FETs with AlOxNy Overlayers
Source abstract
Abstract
In this study, we demonstrate that a room-temperature reactively sputtered aluminum oxynitride (AlO x N y ) overlayer enables both effective doping and pronounced threshold voltage hysteresis in multilayer MoS 2 FETs, while preserving field-effect mobility. Compared to conventional AlO x, the AlO x N y layer introduces trap states that are energetically aligned with the conduction band of MoS 2, facilitating charge exchange across the heterointerface. Capacitance–voltage measurements confirm that nitrogen incorporation reduces the effective fixed charge density, enabling mobility-preserving operation without thermal annealing. Notably, the hysteresis window exhibits a marked expansion above ∼250 K, which correlates with the activation of out-of-plane phonon modes in MoS 2 . These phonons are proposed to assist in activating interfacial trap states within the AlO x N y layer, as supported by temperature-dependent electrical and spectroscopic analyses. While such trap-induced hysteresis may be undesirable for logic circuits, it offers valuable functionality for emerging device architectures─such as in-memory computing and neuromorphic systems─where hysteresis can be exploited. These findings underscore the potential of AlO x N y as a low-temperature-processable dielectric for 2D FETs and advance a new perspective on phonon-assisted interfacial charge modulation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · 2D Materials and Applications · Advancements in Semiconductor Devices and Circuit Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physics
University of Seoul
Graduate School of Semiconductor Materials and Devices Engineering
Ulsan National Institute of Science and Technology
Research Institute of Molecular Alchemy
Gyeongsang National University
Department of Intelligent Semiconductor Engineering
Funding
National Research Foundation of Korea
References (57)
Show source-provided references
1. 10.1063/5.017942410.1063/5.0179424 ↗
2. 10.3390/smartcities703005710.3390/smartcities7030057 ↗
3. 10.1038/s41586-021-04362-w10.1038/s41586-021-04362-w ↗
4. 10.3389/fnins.2016.0033310.3389/fnins.2016.00333 ↗
5. 10.1109/JETCAS.2018.279637910.1109/JETCAS.2018.2796379 ↗
6. 10.1147/JRD.2019.293405010.1147/JRD.2019.2934050 ↗
7. 10.1098/rsta.2019.006110.1098/rsta.2019.0061 ↗
8. 10.1038/nature1067610.1038/nature10676 ↗
9. 10.1016/j.sse.2003.12.02010.1016/j.sse.2003.12.020 ↗
10. Horowitz, M. 1.1 Computing’s Energy Problem (and What We Can Do about It). In 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC); IEEE: San Francisco, CA, USA, 2014; pp 10–14.10.1109/ISSCC.2014.6757323 ↗
11. 10.1109/MDAT.2016.257358610.1109/MDAT.2016.2573586 ↗
12. 10.1145/3296957.317317710.1145/3296957.3173177 ↗
13. Boroumand, A.; Ghose, S.; Akin, B.; Narayanaswami, R.; Oliveira, G. F.; Ma, X.; Shiu, E.; Mutlu, O. Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks. In 2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT); IEEE: Atlanta, GA, USA, 2021; pp 159–172.10.1109/PACT52795.2021.00019 ↗
14. 10.1109/40.59231210.1109/40.592312 ↗
15. 10.1038/s41928-017-0006-810.1038/s41928-017-0006-8 ↗
16. 10.1038/s41928-018-0092-210.1038/s41928-018-0092-2 ↗
17. 10.1016/j.sysarc.2021.10227610.1016/j.sysarc.2021.102276 ↗
18. 10.1038/natrevmats.2017.3310.1038/natrevmats.2017.33 ↗
19. 10.1038/natrevmats.2016.5210.1038/natrevmats.2016.52 ↗
20. 10.1109/JPROC.2013.225343510.1109/JPROC.2013.2253435 ↗
21. Kang, J.; Cao, W.; Xie, X.; Sarkar, D.; Liu, W.; Banerjee, K. Graphene and Beyond-Graphene 2D Crystals for next-Generation Green Electronics. In SPIE Proceedings; George, T.; Islam, M. S.; Dutta, A. K., Eds. Baltimore, Maryland, USA, 2014908305.
22. 10.3390/nano1218323310.3390/nano12183233 ↗
23. 10.1038/nnano.2010.27910.1038/nnano.2010.279 ↗
24. 10.1126/science.aah469810.1126/science.aah4698 ↗
25. 10.1088/2053-1583/3/1/01100610.1088/2053-1583/3/1/011006 ↗
26. 10.1016/j.sse.2019.03.03010.1016/j.sse.2019.03.030 ↗
27. 10.1038/s41586-021-03339-z10.1038/s41586-021-03339-z ↗
28. 10.1021/acsnano.0c0907810.1021/acsnano.0c09078 ↗
29. 10.1038/s41467-020-16766-910.1038/s41467-020-16766-9 ↗
30. 10.1038/s41565-020-0647-z10.1038/s41565-020-0647-z ↗
31. 10.1002/adma.20220604210.1002/adma.202206042 ↗
32. 10.1109/16.54303510.1109/16.543035 ↗
33. 10.34133/2019/161879810.34133/2019/1618798 ↗
34. 10.1016/j.rinp.2022.10562010.1016/j.rinp.2022.105620 ↗
35. 10.1016/j.jallcom.2024.17369910.1016/j.jallcom.2024.173699 ↗
36. 10.1063/5.001082910.1063/5.0010829 ↗
37. 10.1149/09807.0219ecst10.1149/09807.0219ecst ↗
38. 10.1109/TED.2021.306824610.1109/TED.2021.3068246 ↗
39. Sze S. M. · 200610.1002/0470068329 ↗
40. 10.1021/acsnano.8b0916610.1021/acsnano.8b09166 ↗
41. 10.1038/nmat470310.1038/nmat4703 ↗
42. 10.1038/s41928-022-00798-810.1038/s41928-022-00798-8 ↗
43. 10.1063/1.493161710.1063/1.4931617 ↗
44. 10.1039/C6TC04640A10.1039/C6TC04640A ↗
45. 10.1021/acsami.0c0864710.1021/acsami.0c08647 ↗
46. 10.1038/s41467-017-00805-z10.1038/s41467-017-00805-z ↗
47. 10.1021/acsnano.2c1131910.1021/acsnano.2c11319 ↗
48. 10.1166/jnn.2014.989110.1166/jnn.2014.9891 ↗
49. 10.1038/s41928-022-00768-010.1038/s41928-022-00768-0 ↗
50. 10.1021/acs.nanolett.6b0399910.1021/acs.nanolett.6b03999 ↗
51. 10.1103/PhysRevB.83.24521310.1103/PhysRevB.83.245213 ↗
52. 10.1016/j.apsusc.2017.02.12810.1016/j.apsusc.2017.02.128 ↗
53. 10.1002/pssa.20180061610.1002/pssa.201800616 ↗
54. 10.1103/PhysRevB.85.11531710.1103/PhysRevB.85.115317 ↗
55. Campbell J. P. · 2008 · 7210.1109/RELPHY.2008.4558866 ↗
56. 10.1063/1.494240610.1063/1.4942406 ↗
57. 10.1109/JEDS.2018.282993310.1109/JEDS.2018.2829933 ↗