Year: 2025
Type: article
Abstract: This study presents an innovative approach to predicting VCSEL emission characteristics using transformer neural networks. It is demonstrated how to modify the transformer neural network for applications in physics. The 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 conventional neural networks. The transformer architecture also suitable for applications in other fields is proposed. A demo version is available for testing at https://abelonovskii.github.io/opto‐transformer/ .
Source: Laser & Photonics Review
Institution: Lappeenranta-Lahti University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,880
Funder: Etelä-Karjalan Rahasto
Award: G4226500430
OpenAlex ID: W4408384729