Abstract

GONZALEZ VIDAL, J.L.; REYES-BARRANCA, M.A.; VAZQUEZ-ACOSTA, E.N.  and  RAYGOZA PANDURO, J.J.. Sensing system with an artificial neural network based on floating-gate metal oxide semiconductor transistors. Rev. mex. fis. [online]. 2020, vol.66, n.1, pp.91-97.  Epub Nov 27, 2020. ISSN 0035-001X.  https://doi.org/10.31349/revmexfis.66.91.

This paper shows a novel design of a gas sensor system based on artificial neural networks and floating-gate metal oxide semiconductor transistors. Two types of circuits with floating-gate metal oxide semiconductor transistors of minimum dimensions were designed and simulated by Simulink of Matlab; simulations and experimental measurements results were compared, obtaining good expectations. The reason for using floating-gate metal oxide semiconductor is that artificial neural networks can also be implemented with these kinds of devices, since artificial neural networks based on floating-gate metal oxide semiconductors are able to produce pseudo-Gaussian-functions.

These functions give a reliable option to determine gas concentration. A sensitive thin film can be deposited on the floating-gate metal oxide semiconductor floating gate, which produces a charge variation due to the chemical reaction between the sensitive layer and the gas species, modifying the threshold voltage thereby a correlation of drain current of the floating-gate metal oxide semiconductor with gas concentration can be obtained. Therefore, a generator circuit was implemented for the pseudo Gaussian signal with the floating-gate metal oxide semiconductor. This system can be applied in environments with dangerous species such as CO2, CO, methane, propane, among others. Simulations demonstrated that the implemented proposal has a good performance as an alternative method for sensing gas concentrations, compared with conventional sensors.

Keywords : Gas sensor; floating-gate metal oxide semiconductor; artificial neural networks; opamp; 83.30.Tv; 85.40.Bh.

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