Resumen de Sensing system with an artificial neural network based on floating-gate metal oxide semiconductor transistors
J.L. González Vidal, Mario Alfredo Reyes Barranca, E.N. Vázquez Acosta, J.J. Raygoza Panduro
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 CO_(2), 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.
