Predicting VCSEL Emission Properties Using Transformer Neural Networks

Aleksei V. Belonovskii; Elizaveta I. Girshova; Erkki Lähderanta; Mikhail Kaliteevski

doi:10.1002/lpor.202401636

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arXiv · 2407.06039

Predicting VCSEL Emission Properties Using Transformer Neural Networks

Aleksei V. Belonovskii·Elizaveta I. Girshova·Erkki Lähderanta·Mikhail Kaliteevski

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Abstract

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/.

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BibTeX RIS

Aleksei V. Belonovskii, Elizaveta I. Girshova, Erkki Lähderanta, Mikhail Kaliteevski. 2024-07-08. Predicting VCSEL Emission Properties Using Transformer Neural Networks. https://doi.org/10.1002/lpor.202401636

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