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Selectivity enhancement of SiC-FET gas sensors by combining temperature and gate bias cycled operation using multivariate statistics
Linköping University, Department of Physics, Chemistry and Biology, Applied Sensor Science. Linköping University, The Institute of Technology.
University of Saarland, Germany .
Linköping University, Department of Physics, Chemistry and Biology, Applied Sensor Science. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-2817-3574
Linköping University, Department of Physics, Chemistry and Biology, Applied Sensor Science. Linköping University, The Institute of Technology.
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2014 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 193, 931-940 p.Article in journal (Refereed) Published
Abstract [en]

In this paper temperature modulation and gate bias modulation of a gas sensitive field effect transistor based on silicon carbide (SiC-FET) are combined in order to increase the selectivity. Data evaluation based on extracted features describing the shape of the sensor response was performed using multivariate statistics, here by Linear Discriminant Analysis (LDA). It was found that both temperature cycling and gate bias cycling are suitable for quantification of different concentrations of carbon monoxide. However, combination of both approaches enhances the stability of the quantification, respectively the discrimination of the groups in the LDA scatterplot. Feature selection based on the stepwise LDA algorithm as well as selection based on the loadings plot has shown that features both from the temperature cycle and from the bias cycle are equally important for the identification of carbon monoxide, nitrogen dioxide and ammonia. In addition, the presented method allows discrimination of these gases independent of the gas concentration. Hence, the selectivity of the FET is enhanced considerably.

Place, publisher, year, edition, pages
Elsevier , 2014. Vol. 193, 931-940 p.
Keyword [en]
SiC-FET; Temperature modulation; Gate bias modulation; Selectivity; Feature extraction; Pattern recognition
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-104641DOI: 10.1016/j.snb.2013.12.030ISI: 000330113600130OAI: oai:DiVA.org:liu-104641DiVA: diva2:698196
Available from: 2014-02-20 Created: 2014-02-20 Last updated: 2017-12-06

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Bur, ChristianLloyd Spetz, AnitaAndersson, Mike

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