Predicting VCSEL Emission Properties using Transformer Neural Networks

被引：0

作者：

Belonovskii, Aleksei ^{[1
]}

Girshova, Elizaveta ^{[1
]}

Lahderanta, Erkki ^{[1
]}

Kaliteevski, Mikhail ^{[1
]}

机构：

[1] Lappeenranta Univ Technol LUT, Skinnarilankatu 34, Lappeenranta 53850, Finland

来源：

LASER & PHOTONICS REVIEWS | 2025年 / 19卷 / 12期

关键词：

computational photonics; machine learning; neural network applications; transformer neural networks; VCSEL; INVERSE DESIGN;

D O I：

10.1002/lpor.202401636

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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/.

引用

页数：13

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