Modeling of temperature‑dependent photoluminescence of GaN epilayer by artificial neural network
Date
22.06.2023Metadata
Show full item recordAbstract
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 temperaturedependent
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.