Deep Neural Network Based Modelling of Chemisorption Process on Surface of Oxide Based Gas Sensors

The sensor response of the metal oxide based gas sensor has been simulated using Deep Neural Network (DNN) model. The neural network designed for the modelling of the sensor has single input layer, three hidden layers and single output layer. The linear regression algorithm has been used to compute the electrical conductance of the sensor at given temperature and pressure. The data generated through modified Wolkenstein method has been used for training, validation and testing of the developed network. The data for materials Tin (IV) oxide (SnO₂), Tin (II) oxide (SnO) and Copper (I) oxide (Cu₂O) with different E_g values has been utilized. The other input parameters like Temperature, N_D, N_C, N_V, E_F−E_SSand E_CS−E_F are varied for the specific range to collect a variety of data for calculation of electrical conductance of the sensor. The total data used for training, validation and testing was 1,90,512 data points. The plots for training, validation and testing phase have been plotted. The sensor response computed through the proposed model is validated with the results of already published mathematical model. The sensor response shows steep change when the gas concentration of the target gas reaches above 10^-8 atm. The proposed model can be retrained or transfer learning can be applied for using the same model for other types of materials for gas sensing applications.