Optimised temperature modulation of metal oxide micro-hotplate gas sensors through multilevel pseudo random sequences

We show how it is possible to optimise a multi-frequency signal to be used in the modulation of the operating temperature of an integrated gas sensor microarray. In the first step, a multilevel pseudo random sequence (ML-PRS), which allows for modulating the operating temperature of the sensors in a wide frequency range, is used to obtain an estimate of the impulse response of each microsensor-gas system. ML-PRS are interesting because they help to reduce the effects of noise and non-linearity as experienced with gas sensors. In the second step, by analysing the spectral components of the impulse response estimates, the modulating frequencies that better help in discriminating and quantifying the gases and gas mixtures considered are found. Finally, by selecting a subset of the best modulating frequencies, an optimised multi-frequency temperature-modulating signal can be synthesised. The method is illustrated by analysing different concentrations of NH3, NO2 and their mixtures using a microarray of WO3-based gas sensors, but it can be further applied to any given gas analysis problem.