journal article·2025-04-04Open access

Analytical modeling and experimental characterization of drift in electrolyte-gated graphene field-effect transistors

João Mouro, Telma Domingues, Tiago Pereira, Rui Campos, Jérôme Borme, Pedro Alpuim

Abstract

Electrolyte-gated graphene field-effect transistors are increasingly attractive as platforms for high-sensitivity and low-detection limit biosensing or in synaptic or memristive elements used in neuromorphic computing. However, their electrical stability and noise have yet to be thoroughly analyzed and understood. In this work, we have undertaken a comprehensive experimental characterization of the dynamic response of the drift in electrolyte-gated graphene field-effect transistors under various measurement conditions. We then developed an analytical model, which was phenomenologically validated for all observed drift phenomena and fitted to the experimental data. The model is based on charge trapping at the silicon oxide substrate defects in contact with the graphene channel. The electron transitions are made possible by the absorption of phonons to overcome the energetic barrier leading to the new state. This in-depth understanding of these devices’ responses is essential to fully exploit their behavior in applications and standardize practices amongst the community.
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Research context

OpenAlex topics: Graphene research and applications · Advanced Memory and Neural Computing · Analytical Chemistry and Sensors

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References (50)

Show source-provided references
1. Béraud, A. et al. Graphene field-effect transistors as bioanalytical sensors: design, operation and performance. Analyst 146, 403–428 (2021).10.1039/D0AN01661F ↗
2. Abrantes, M. et al. Ultrasensitive dopamine detection with graphene aptasensor multitransistor arrays. J. Nanobiotechnol. 20, https://doi.org/10.1186/s12951-022-01695-0 (2022).10.1186/s12951-022-01695-0 ↗
3. Palacio, I. et al. Attomolar detection of hepatitis C virus core protein powered by molecular antenna-like effect in a graphene field-effect aptasensor. Biosens. Bioelectron. 222, https://doi.org/10.1016/j.bios.2022.115006 (2023).10.1016/j.bios.2022.115006 ↗
4. Campos, R. et al. Attomolar label-free detection of DNA hybridization with electrolyte-gated graphene field-effect transistors. ACS Sens. 4, 286–293 (2019).10.1021/acssensors.8b00344 ↗
5. Purwidyantri, A. et al. Integrated approach from sample-to-answer for grapevine varietal identification on a portable graphene sensor chip. ACS Sens. 8, 640–654 (2023).10.1021/acssensors.2c02090 ↗
6. Pannone, A. et al. Robust chemical analysis with graphene chemosensors and machine learning. Nature https://doi.org/10.1038/s41586-024-08003-w (2024).10.1038/s41586-024-08003-w ↗
7. Illarionov, Y. Y. et al. The role of charge trapping in MoS2/SiO2 and MoS2/hBN field-effect transistors. 2D Mater. 3, https://doi.org/10.1088/2053-1583/3/3/035004 (2016).10.1088/2053-1583/3/3/035004 ↗
8. Illarionov, Y. Y. et al. Energetic mapping of oxide traps in MoS2 field-effect transistors. 2D Mater. 4, https://doi.org/10.1088/2053-1583/aa734a (2017).10.1088/2053-1583/aa734a ↗
9. Illarionov, Y. Y. et al. Insulators for 2D nanoelectronics: the gap to bridge. Nat. Commun. 11, https://doi.org/10.1038/s41467-020-16640-8 (2020).10.1038/s41467-020-16640-8 ↗
10. Knobloch, T. et al. Improving stability in two-dimensional transistors with amorphous gate oxides by Fermi-level tuning. Nat. Electron. 5, 356–366 (2022).10.1038/s41928-022-00768-0 ↗
11. Syed, G. S., Zhou, Y., Warner, J. & Bhaskaran, H. Atomically thin optomemristive feedback neurons. Nat. Nanotechnol. 18, 1036–1043 (2023).10.1038/s41565-023-01391-6 ↗
12. Schranghamer, T. F., Oberoi, A. & Das, S. Graphene memristive synapses for high precision neuromorphic computing. Nat. Commun. 11, 1–11 (2020).10.1038/s41467-020-19203-z ↗
13. Walters, B., Jacob, M. V., Amirsoleimani, A. & Rahimi Azghadi, M. A review of graphene-based memristive neuromorphic devices and circuits. Adv. Intell. Syst. 5, https://doi.org/10.1002/aisy.202300136 (2023).10.1002/aisy.202300136 ↗
14. Mann, H. K. et al. Optical control of multiple resistance levels in graphene for memristic applications. npj 2D Mater. Appl. 1–7. https://doi.org/10.1038/s41699-024-00503-7 (2024).10.1038/s41699-024-00503-7 ↗
15. Vieira, N. C. S. et al. Graphene field-effect transistor array with integrated electrolytic gates scaled to 200 mm. J. Phys. Condens. Matter 28, 085302 (2016).10.1088/0953-8984/28/8/085302 ↗
16. Torricelli, F. et al. Electrolyte-gated transistors for enhanced performance bioelectronics. Nat. Rev. Methods Primers 1, https://doi.org/10.1038/s43586-021-00065-8 (2021).10.1038/s43586-021-00065-8 ↗
17. Cabral, P. D. et al. Clean-room lithographical processes for the fabrication of graphene biosensors. Materials 13, 1–23 (2020).10.3390/ma13245728 ↗
18. Grasser, T. Stochastic charge trapping in oxides: from random telegraph noise to bias temperature instabilities. Microelectron. Reliab. 39–70. https://doi.org/10.1016/j.microrel.2011.09.002 (2012).10.1016/j.microrel.2011.09.002 ↗
19. Reisinger, H., Grasser, T., Gustin, W. & Schlünder, C. The statistical analysis of individual defects constituting NBTI and its implications for modeling DC- and AC-stress. In IEEE International Reliability Physics Symposium Proceedings, 7–15 (IEEE, 2010).10.1109/IRPS.2010.5488858 ↗
20. Illarionov, Y. Y. et al. Long-term stability and reliability of black phosphorus field-effect transistors. ACS Nano 10, 9543–9549 (2016).10.1021/acsnano.6b04814 ↗
21. Illarionov, Y. Y. et al. Bias-temperature instability in single-layer graphene field-effect transistors. Appl. Phys. Lett. 105, https://doi.org/10.1063/1.4897344 (2014).10.1063/1.4897344 ↗
22. Kaczer, B., Toledano-Luque, M., Goes, W., Grasser, T. & Groeseneken, G. Gate current random telegraph noise and single defect conduction. Microelectron. Eng. 109, 123–125 (2013).10.1016/j.mee.2013.03.110 ↗
23. Da Silva, M. B., Both, T. H. & Wirth, G. I. Random telegraph noise modeling for circuit analysis: RTN in ring oscillators. IEEE J. Electron Devices Soc. 10, 459–465 (2022).10.1109/JEDS.2022.3147386 ↗
24. Rzepa, G. et al. Comphy—a compact-physics framework for unified modeling of BTI. Microelectron. Reliab. 85, 49–65 (2018).10.1016/j.microrel.2018.04.002 ↗
25. Grasser, T. et al. The time dependent defect spectroscopy (TDDS) for the characterization of the bias temperature instability. In IEEE International Reliability Physics Symposium, 16–25 (IEEE, 2010).10.1109/IRPS.2010.5488859 ↗
26. Grasser, T. Bias Temperature Instability for Devices and Circuits (Springer New York, Heidelberg Dordrecht London, 2014).10.1007/978-1-4614-7909-3 ↗
27. Merino, J. P. et al. Ethanol solvation of polymer residues in graphene solution-gated field effect transistors. ACS Sustain. Chem. Eng. 12, 9133–9143 (2024).10.1021/acssuschemeng.4c01538 ↗
28. Bartošík, M. et al. Mechanism and suppression of physisorbed-water-caused hysteresis in graphene FET sensors. ACS Sens. 5, 2940–2949 (2020).10.1021/acssensors.0c01441 ↗
29. Caridad, J. M. et al. A graphene-edge ferroelectric molecular switch. Nano Lett. 18, 4675–4683 (2018).10.1021/acs.nanolett.8b00797 ↗
30. Miyakawa, N. et al. Drift suppression of solution-gated graphene field-effect transistors by cation doping for sensing platforms. Sensors 21, 1–11 (2021).10.3390/s21227455 ↗
31. Jia, X. et al. Kinetic ionic permeation and interfacial doping of supported graphene. Nano Lett. 19, 9029–9036 (2019).10.1021/acs.nanolett.9b04053 ↗
32. Kim, J. S. et al. Between scylla and charybdis: hydrophobic graphene-guided water diffusion on hydrophilic substrates. Sci. Rep. 3, 1–6 (2013).10.1038/srep02309 ↗
33. Lopes, L. C., Santos, A. & Bueno, P. R. Measuring quantum conductance and capacitance of graphene using impedance-derived capacitance spectroscopy. Carbon 184, 821–827 (2021).10.1016/j.carbon.2021.08.055 ↗
34. Xia, J., Chen, F., Li, J. & Tao, N. Measurement of the quantum capacitance of graphene. Nat. Nanotechnol. 4, 505–509 (2009).10.1038/nnano.2009.177 ↗
35. Wu, J. Understanding the electric double-layer structure, capacitance, and charging dynamics. Chem. Rev. 122, 10821–10859 (2022).10.1021/acs.chemrev.2c00097 ↗
36. Das, A. et al. Monitoring dopants by Raman scattering in an electrochemically top-gated graphene transistor. Nat. Nanotechnol. 3, 210–215 (2008).10.1038/nnano.2008.67 ↗
37. Peres, N. M. R. Colloquium: the transport properties of graphene: an introduction. Rev. Mod. Phys. 82, 2673–2700 (2010).10.1103/RevModPhys.82.2673 ↗
38. Zuccaro, L., Krieg, J., Desideri, A., Kern, K. & Balasubramanian, K. Tuning the isoelectric point of graphene by electrochemical functionalization. Sci. Rep. 5, 11794 (2015).10.1038/srep11794 ↗
39. Thiele, S. A., Schaefer, J. A. & Schwierz, F. Modeling of graphene metal-oxide-semiconductor field-effect transistors with gapless large-area graphene channels. J. Appl. Phys. 107, https://doi.org/10.1063/1.3357398 (2010).10.1063/1.3357398 ↗
40. Kim, T., Park, S. & Jeon, S. Fast and slow transient charging of oxide semiconductor transistors. Sci. Rep. 7, https://doi.org/10.1038/s41598-017-12155-3 (2017).10.1038/s41598-017-12155-3 ↗
41. Xu, K. et al. Pulse dynamics of electric double layer formation on all-solid-state graphene field-effect transistors. ACS Appl. Mater. Interfaces 10, 43166–43176 (2018).10.1021/acsami.8b13649 ↗
42. Woeppel, A. et al. Single- versus dual-ion conductors for electric double layer gating: finite element modeling and Hall-effect measurements. ACS Appl. Mater. Interfaces 12, 40850–40858 (2020).10.1021/acsami.0c08653 ↗
43. Park, Y., Baac, H. W., Heo, J. & Yoo, G. Thermally activated trap charges responsible for hysteresis in multilayer MoS2 field-effect transistors. Appl. Phys. Lett. 108, https://doi.org/10.1063/1.4942406 (2016).10.1063/1.4942406 ↗
44. Delavari, N. et al. Nernst–Planck–Poisson analysis of electrolyte-gated organic field-effect transistors. J. Phys. D Appl. Phys. 54, https://doi.org/10.1088/1361-6463/ac14f3 (2021).10.1088/1361-6463/ac14f3 ↗
45. Wang, H., Wu, Y., Cong, C., Shang, J. & Yu, T. Hysteresis of electronic transport in graphene transistors. ACS Nano 4, 7221–7228 (2010).10.1021/nn101950n ↗
46. Grasser, T. et al. The ‘permanent’ component of NBTI: composition and annealing. In IEEE International Reliability Physics Symposium Proceedings, 6A. 2.1-6A. 2.9 (IEEE, 2011).10.1109/IRPS.2011.5784543 ↗
47. Iqbal, H. F. et al. Suppressing bias stress degradation in high performance solution processed organic transistors operating in air. Nat. Commun. 12, 1–10 (2021).10.1038/s41467-021-22683-2 ↗
48. Nagumo, T., Takeuchi, K., Hase, T. & Hayashi, Y. Statistical characterization of trap position, energy, amplitude and time constants by RTN measurement of multiple individual traps. In Technical Digest—International Electron Devices Meeting IEDM, 28.3.1–28.3.4 (IEEE, San Francisco, 2010).10.1109/IEDM.2010.5703437 ↗
49. Wagner, P. J., Grasser, T., Reisinger, H. & Kaczer, B. Oxide traps in MOS transistors: semi-automatic extraction of trap parameters from time dependent defect spectroscopy. In Proceedings of the International Symposium on the Physical and Failure Analysis of Integrated Circuits, IPFA, 1–5 (IEEE, 2010).10.1109/IPFA.2010.5532233 ↗
50. Chen, S. et al. Review on two-dimensional material-based field-effect transistor biosensors: accomplishments, mechanisms, and perspectives. J. Nanobiotechnol. 21, 1–32 (2023).10.1186/s12951-023-01898-z ↗