Sensing system with an artificial neural network based on floating-gate metal oxide semiconductor transistors

Abstract 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.

Bibliographic Details

Main Authors:	González Vidal,J.L., Reyes-Barranca,M.A., Vázquez-Acosta,E.N., Raygoza Panduro,J.J.
Format:	Digital revista
Language:	English
Published:	Sociedad Mexicana de Física 2020
Online Access:	http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0035-001X2020000100091
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