Characterization of SiNx grown at different nitrogen flow and prediction of refractive index using artificial neural networks

SiN_x films were grown on silicon substrates by Radio Frequency (RF) magnetron sputtering deposition. The effect of nitrogen flow on the structural and optical properties of the obtained films was investigated using X-ray diffraction, Scanning Electron Microscopy (SEM), UV–Vis–NIR spectrophotometer and spectroscopic ellipsometer, respectively. XRD spectra of the films showed that all films belong to amorphous structure. SEM photographs of SiN_x films were analyzed. As a result of the analysis, it was observed that the surfaces of the films had a homogeneous and smooth structure as the nitrogen flow increased. The total and diffuse reflectance spectra of the films were measured and the energy band gaps of the films were determined using the Kubelka-Munk function by using the diffuse reflectance. It was observed that the energy band gap changed as the nitrogen percentage increased. The refractive index of all films was obtained as a function of temperature using a spectroscopic ellipsometer. In the second part of this study, we focused on predicting the temperature dependent refractive indices of the nitrogen flow-dependent films using Artificial Neural Networks (ANN). For the training of the ANN model, wavelength and temperature values from experimental data were used as input and refractive index as output parameters. The simulation and prediction results obtained from this model are compared with the experimental data and interpreted. It is concluded that the ANN approach is suitable for simulating and predicting the temperature dependent refractive index. The models successfully trained with ANN will be especially preferred for predicting the refractive indices of SiN_x films, which cannot be measured experimentally, thus providing predictions in non-experimental ranges. In particular, the results obtained by focusing on the ability of the developed artificial neural network (ANN) models to predict the optical properties of SiN_x films and their potential to provide information in non-experimental conditions, offer a new approach to quickly and effectively evaluate the optical properties of SiN_x films. This approach reveals the importance of artificial intelligence-based methods in materials characterization studies.