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 MOS Transistors (FGMOS). Two types of circuits with FGMOS transistors of minimum dimensions were designed and simulated by Simulink of Matlab; simulations and experimental measurements results were compared obtaining good expectations. The reason of using FGMOS is that ANN can also be implemented with these kinds of devices, since ANN’s based on FGMOS are able to produce pseudo Gaussian-functions. These functions give a reliable option to determine the gas concentration. A sensitive thin film can be deposited on the FGMOS’s 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 FGMOS with gas concentration can be obtained. Therefore, a generator circuit was implemented for the pseudo Gaussian signal with FGMOS. 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.