2006
DOI: 10.1109/jsen.2005.844353
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Temperature feedback control for improving the stability of a semiconductor-metal-oxide (SMO) gas sensor

Abstract: Stability is a major concern of semiconductor-metal-oxide (SMO) gas sensors in practical applications, as they may cause false alarm problems. Ambient temperature is a major factor affecting the SMO gas sensor's stability. In this paper, we use a novel way to improve temperature stability of SMO (tin oxide) gas sensors by applying a temperature feedback control circuits which are compatible with our microelectromechanical systems senor fabrication. A built-in platinum temperature sensor can precisely detect th… Show more

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Cited by 32 publications
(6 citation statements)
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References 17 publications
(11 reference statements)
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“…The electron-hole pair's recombination may be facilitated by the bandgap's narrowing. The temperature typically decreases bandgap stability [38], [39]. If the fact that an increase in the amplitude of the atomic vibrations is accompanied by an increase in the thermal energy is considered, this behavior can be better comprehended.…”
Section: Resultsmentioning
confidence: 99%
“…The electron-hole pair's recombination may be facilitated by the bandgap's narrowing. The temperature typically decreases bandgap stability [38], [39]. If the fact that an increase in the amplitude of the atomic vibrations is accompanied by an increase in the thermal energy is considered, this behavior can be better comprehended.…”
Section: Resultsmentioning
confidence: 99%
“…These sensors have the advantages like improved sensitivity and reduced cost with reliability issues. In [64], the authors developed a setup that supplies regulated current by using feedback control for temperature compensation. By overlaying appropriate materials on metal oxides, such as zeolites, the selectivity of these sensors may be enhanced [65].…”
Section: Gas Detectorsmentioning
confidence: 99%
“…On top of that, the data distributions of different gases are assumed the same, which is in fact not true all the time. To tackle the drift problem, two other effective supervised techniques, namely, Component Correction-based [11][12][13] and Sequential Minimal Optimisation-based [14,15] techniques, were introduced. However, Sequential Minimal Optimisation-based techniques might update the model by following the incorrect reference label sometimes.…”
Section: Related Workmentioning
confidence: 99%