Prediction of Anomalous Variation in GaN-based Chemical Sensors

Jeremy Gillbanks; Gilberto Umana-Membreno; Matthew Myers; Brett D. Nener; Giacinta Parish

doi:10.1109/ICCAIS56082.2022.9990542

Prediction of Anomalous Variation in GaN-based Chemical Sensors

Jeremy Gillbanks, Gilberto Umana-Membreno, Matthew Myers, Brett D. Nener, Giacinta Parish

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

Abstract

We present the result of investigations into anomalous drift behavior in AlGaN/GaN-based chemical sensors; a problem that must be addressed in such sensors to achieve their great promise as a portable sensor technology. Leakage current measurements undertaken on AlGaN/GaN-based sensor devices were fitted with a parallel diode model. Some sensors were then destructively tested using phosphoric acid etchant to decorate the defects, allowing the density of conductive screw dislocations to be quantified. A strong correlation between diode saturation current and defect density was then established. The remaining devices were encapsulated as working sensors and left in an ionic solution to determine the normalized root mean square error as a measure of drift. This also exhibited a strong correlation with the saturation current, thus establishing a link between semiconductor defect density, leakage current, and uncorrelated drift over time for AlGaN/GaN chemical sensors.

Original language	English
Title of host publication	2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	397-404
Number of pages	8
ISBN (Electronic)	9781665452489
DOIs	https://doi.org/10.1109/ICCAIS56082.2022.9990542
Publication status	Published - 2022
Event	11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022 - Hanoi, Viet Nam Duration: 21 Nov 2022 → 24 Nov 2022

Publication series

Name	2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022

Conference

Conference	11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022
Country/Territory	Viet Nam
City	Hanoi
Period	21/11/22 → 24/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICCAIS56082.2022.9990542

Cite this

Gillbanks, J., Umana-Membreno, G., Myers, M., Nener, B. D., & Parish, G. (2022). Prediction of Anomalous Variation in GaN-based Chemical Sensors. In 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022 (pp. 397-404). (2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCAIS56082.2022.9990542

Gillbanks, Jeremy ; Umana-Membreno, Gilberto ; Myers, Matthew et al. / Prediction of Anomalous Variation in GaN-based Chemical Sensors. 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022. IEEE, Institute of Electrical and Electronics Engineers, 2022. pp. 397-404 (2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022).

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title = "Prediction of Anomalous Variation in GaN-based Chemical Sensors",

abstract = "We present the result of investigations into anomalous drift behavior in AlGaN/GaN-based chemical sensors; a problem that must be addressed in such sensors to achieve their great promise as a portable sensor technology. Leakage current measurements undertaken on AlGaN/GaN-based sensor devices were fitted with a parallel diode model. Some sensors were then destructively tested using phosphoric acid etchant to decorate the defects, allowing the density of conductive screw dislocations to be quantified. A strong correlation between diode saturation current and defect density was then established. The remaining devices were encapsulated as working sensors and left in an ionic solution to determine the normalized root mean square error as a measure of drift. This also exhibited a strong correlation with the saturation current, thus establishing a link between semiconductor defect density, leakage current, and uncorrelated drift over time for AlGaN/GaN chemical sensors.",

keywords = "aluminium gallium nitride, chemical sensors, HEMTs, ion sensing, materials testing, reference-electrode free, semiconductor device stability",

author = "Jeremy Gillbanks and Gilberto Umana-Membreno and Matthew Myers and Nener, {Brett D.} and Giacinta Parish",

note = "Funding Information: This work was partially funded by the Australian Research Council [DP140100827]. This work used the Western Australian node of the NCRIS-enabled Australian National Fabrication Facility (ANFF). We acknowledge the support from the Western Australian Government s Department of Jobs, Tourism, Science Innovation. JG is funded by a University Postgraduate Award (UPA) provided by the Australian Government and The University of Western Australia. We would like to acknowledge the assistance of Ken Fogden, Roger Jeffery, and Adrian Keating in designing and operating the experimental setups. We also thank the contributions of the Mishra group at University of California, Santa Barbara for the AlGaN/GaN wafer growth. Funding Information: ACKNOWLEDGMENT This work was partially funded by the Australian Research Council [DP140100827]. This work used the Western Australian node of the NCRIS-enabled Australian National Fabrication Facility (ANFF). We acknowledge the support from the Western Australian Government{\textquoteright}s Department of Jobs, Tourism, Science Innovation. JG is funded by a University Postgraduate Award (UPA) provided by the Australian Government and The University of Western Australia. We would like to acknowledge the assistance of Ken Fogden, Roger Jeffery, and Adrian Keating in designing and operating the experimental setups. We also thank the contributions of the Mishra group at University of California, Santa Barbara for the AlGaN/GaN wafer growth. Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022 ; Conference date: 21-11-2022 Through 24-11-2022",

year = "2022",

doi = "10.1109/ICCAIS56082.2022.9990542",

language = "English",

series = "2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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Gillbanks, J, Umana-Membreno, G , Myers, M , Nener, BD & Parish, G 2022, Prediction of Anomalous Variation in GaN-based Chemical Sensors. in 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022. 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022, IEEE, Institute of Electrical and Electronics Engineers, pp. 397-404, 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022, Hanoi, Viet Nam, 21/11/22. https://doi.org/10.1109/ICCAIS56082.2022.9990542

Prediction of Anomalous Variation in GaN-based Chemical Sensors. / Gillbanks, Jeremy; Umana-Membreno, Gilberto ; Myers, Matthew et al.
2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022. IEEE, Institute of Electrical and Electronics Engineers, 2022. p. 397-404 (2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022).

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Prediction of Anomalous Variation in GaN-based Chemical Sensors

AU - Gillbanks, Jeremy

AU - Umana-Membreno, Gilberto

AU - Myers, Matthew

AU - Nener, Brett D.

AU - Parish, Giacinta

N1 - Funding Information: This work was partially funded by the Australian Research Council [DP140100827]. This work used the Western Australian node of the NCRIS-enabled Australian National Fabrication Facility (ANFF). We acknowledge the support from the Western Australian Government s Department of Jobs, Tourism, Science Innovation. JG is funded by a University Postgraduate Award (UPA) provided by the Australian Government and The University of Western Australia. We would like to acknowledge the assistance of Ken Fogden, Roger Jeffery, and Adrian Keating in designing and operating the experimental setups. We also thank the contributions of the Mishra group at University of California, Santa Barbara for the AlGaN/GaN wafer growth. Funding Information: ACKNOWLEDGMENT This work was partially funded by the Australian Research Council [DP140100827]. This work used the Western Australian node of the NCRIS-enabled Australian National Fabrication Facility (ANFF). We acknowledge the support from the Western Australian Government’s Department of Jobs, Tourism, Science Innovation. JG is funded by a University Postgraduate Award (UPA) provided by the Australian Government and The University of Western Australia. We would like to acknowledge the assistance of Ken Fogden, Roger Jeffery, and Adrian Keating in designing and operating the experimental setups. We also thank the contributions of the Mishra group at University of California, Santa Barbara for the AlGaN/GaN wafer growth. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - We present the result of investigations into anomalous drift behavior in AlGaN/GaN-based chemical sensors; a problem that must be addressed in such sensors to achieve their great promise as a portable sensor technology. Leakage current measurements undertaken on AlGaN/GaN-based sensor devices were fitted with a parallel diode model. Some sensors were then destructively tested using phosphoric acid etchant to decorate the defects, allowing the density of conductive screw dislocations to be quantified. A strong correlation between diode saturation current and defect density was then established. The remaining devices were encapsulated as working sensors and left in an ionic solution to determine the normalized root mean square error as a measure of drift. This also exhibited a strong correlation with the saturation current, thus establishing a link between semiconductor defect density, leakage current, and uncorrelated drift over time for AlGaN/GaN chemical sensors.

AB - We present the result of investigations into anomalous drift behavior in AlGaN/GaN-based chemical sensors; a problem that must be addressed in such sensors to achieve their great promise as a portable sensor technology. Leakage current measurements undertaken on AlGaN/GaN-based sensor devices were fitted with a parallel diode model. Some sensors were then destructively tested using phosphoric acid etchant to decorate the defects, allowing the density of conductive screw dislocations to be quantified. A strong correlation between diode saturation current and defect density was then established. The remaining devices were encapsulated as working sensors and left in an ionic solution to determine the normalized root mean square error as a measure of drift. This also exhibited a strong correlation with the saturation current, thus establishing a link between semiconductor defect density, leakage current, and uncorrelated drift over time for AlGaN/GaN chemical sensors.

KW - aluminium gallium nitride

KW - chemical sensors

KW - HEMTs

KW - ion sensing

KW - materials testing

KW - reference-electrode free

KW - semiconductor device stability

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DO - 10.1109/ICCAIS56082.2022.9990542

M3 - Conference paper

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T3 - 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022

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EP - 404

BT - 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022

Y2 - 21 November 2022 through 24 November 2022

ER -

Gillbanks J, Umana-Membreno G , Myers M , Nener BD , Parish G. Prediction of Anomalous Variation in GaN-based Chemical Sensors. In 2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022. IEEE, Institute of Electrical and Electronics Engineers. 2022. p. 397-404. (2022 11th International Conference on Control, Automation and Information Sciences, ICCAIS 2022). doi: 10.1109/ICCAIS56082.2022.9990542

Prediction of Anomalous Variation in GaN-based Chemical Sensors

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Transistor-based sensor technology for fast, reliable and accurate in situ monitoring of recycled wastewater.

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Prediction of Anomalous Variation in GaN-based Chemical Sensors

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Publication series

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UN SDGs

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Transistor-based sensor technology for fast, reliable and accurate in situ monitoring of recycled wastewater.

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