Predicting VCSEL Emission Properties Using Transformer Neural Networks

2024/07/08 by Aleksei V. Belonovskii, Belonovskii, Aleksei V., Elizaveta I. Girshova +5 · 2 citations
Chemical Engineering · Engineering · #68T01 #68T05 #78A60 #78M50 #81V80 #Analytical Chemistry and Sensors #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #I.2.0 #I.2.6 #J.2 #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Photonic and Optical Devices #Semiconductor Lasers and Optical Devices

paper · pdf · doi:10.48550/arxiv.2407.06039

openalex publication_date 2024/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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