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Open AccessArticle

Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis

1
MNF—The Micro Nano characterization and fabrication Facility, Bruno Kessler Foundation, Via Sommarive 18, 38123 Trento, Italy
2
Faculty of Science and Technology, Free University of Bolzano-Bozen, Piazza Università 5, 39100 Bolzano, Italy
3
MST—MicroSystem Technology Group, Bruno Kessler Foundation, Via Sommarive 18, 38123 Trento, Italy
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Eduard Llobet
Sensors 2021, 21(3), 783; https://doi.org/10.3390/s21030783
Received: 31 December 2020 / Revised: 20 January 2021 / Accepted: 22 January 2021 / Published: 25 January 2021
(This article belongs to the Special Issue Advanced Micro and Nano Technologies for Gas Sensing)
The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process has been achieved, thanks to the advances in micro- and nanofabrication for micro-electro-mechanical system (MEMS) technologies. In addition, the use of innovative materials and smaller low-power consumption silicon microheaters led to the development of high-performance gas sensors. Various heater layouts were investigated to optimize the temperature distribution on the membrane, and a suspended membrane configuration was exploited to avoid heat loss by conduction through the silicon bulk. However, there is a lack of comprehensive studies focused on predictive models for the optimization of the thermal and mechanical properties of a microheater. In this work, three microheater layouts in three membrane sizes were developed using the microfabrication process. The performance of these devices was evaluated to predict their thermal and mechanical behaviors by using both experimental and theoretical approaches. Finally, a statistical method was employed to cross-correlate the thermal predictive model and the mechanical failure analysis, aiming at microheater design optimization for gas-sensing applications. View Full-Text
Keywords: silicon microheaters; chemoresistive gas sensors; predictive thermal model; mechanical failure analysis; response surface method silicon microheaters; chemoresistive gas sensors; predictive thermal model; mechanical failure analysis; response surface method
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MDPI and ACS Style

Gaiardo, A.; Novel, D.; Scattolo, E.; Crivellari, M.; Picciotto, A.; Ficorella, F.; Iacob, E.; Bucciarelli, A.; Petti, L.; Lugli, P.; Bagolini, A. Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis. Sensors 2021, 21, 783. https://doi.org/10.3390/s21030783

AMA Style

Gaiardo A, Novel D, Scattolo E, Crivellari M, Picciotto A, Ficorella F, Iacob E, Bucciarelli A, Petti L, Lugli P, Bagolini A. Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis. Sensors. 2021; 21(3):783. https://doi.org/10.3390/s21030783

Chicago/Turabian Style

Gaiardo, Andrea; Novel, David; Scattolo, Elia; Crivellari, Michele; Picciotto, Antonino; Ficorella, Francesco; Iacob, Erica; Bucciarelli, Alessio; Petti, Luisa; Lugli, Paolo; Bagolini, Alvise. 2021. "Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis" Sensors 21, no. 3: 783. https://doi.org/10.3390/s21030783

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