Modeling of temperature-dependent photoluminescence of GaN epilayer by artificial neural network

Artificial neural networks (ANNs) are a type of machine learning model that are designed to mimic the structure and function of biological neurons. They are particularly well-suited for tasks such as image and speech recognition, natural language processing, and prediction tasks. The success of an ANN in modeling a particular dataset depends on factors such as the size and quality of the dataset, the complexity of the model, and the choice of training algorithms. High representation rate of a system in the data set can improve the performance of the ANN model. The study we described is focused on using artificial neural networks (ANNs) to model temperature-dependent photoluminescence (PL) characterization of GaN epilayers grown on patterned sapphire substrates (PSS) using the metalorganic chemical vapor deposition (MOCVD) technique. The ANN model is trained using temperature and wavelength as input parameters and intensity as the output parameter, with the goal of accurately predicting the PL intensity of the GaN epilayer as a function of temperature and wavelength. The model is trained using a large set of experimental data and then tested using data that was not presented to the model during training. The results of the study suggest that ANN modeling methodology is an effective and accurate way of modeling temperature-dependent PL of GaN epilayers grown on PSS. The results of the study suggest that ANN modeling methodology can be used to accurately predict the temperature-dependent PL of GaN epilayers grown on PSS. This means that it may be possible to reduce the number of required experimental measurements by using the ANN model to predict PL intensity at different temperatures, based on a smaller set of experimental measurements. This could potentially save time and resources, while still obtaining accurate information about the optical behavior of GaN-based materials at different temperatures.