This study presents an innovative approach to predicting VCSEL emission characteristics using transformer neural networks. We demonstrate how to modify the transformer neural network for applications in physics. Our model achieved high accuracy in predicting parameters such as VCSEL's eigenenergy, quality factor, and threshold material gain, based on the laser's structure. This model trains faster and predicts more accurately compared to traditional neural networks. The transformer architecture we propose is also suitable for applications in other fields. A demo version is available for testing at https://abelonovskii.github.io/opto-transformer/.
@article{arxiv.2407.06039,
title = {Predicting VCSEL Emission Properties Using Transformer Neural Networks},
author = {Aleksei V. Belonovskii and Elizaveta I. Girshova and Erkki Lähderanta and Mikhail Kaliteevski},
journal= {arXiv preprint arXiv:2407.06039},
year = {2025}
}
Comments
20 pages including appendix, 11 figures, 3 tables, intended for submission to a peer-reviewed journal