Next-Value Prediction of Beam Waists From Mixed Pitch Grating Using Simplified Transformer Model

In this study, a simplified transformer model is used to predict the beam waist of 1,092 nm light coupled out from SiN-based mixed pitch gratings at various heights. The beam waists data at various heights above the grating is first compiled. Then, we used a sequence of the current beam waist values, z-positions, and the computed mathematical indicators (features) to predict the next beam waist value (labels). Optimized transformer model yields average percentage error (APE) of 6.6% between the predicted and actual beam waists, which corresponds to 93.4% prediction accuracy. This study provides a pioneering approach to using natural language processing model to perform predictive modelling on photonics data, and possible extrapolation of photonics data using transformer model.

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Publication Details

Journal
IEEE photonics journal
Published
2024-08-13
DOI
https://doi.org/10.1109/jphot.2024.3442169
Citations
3
Primary Topic
Photonic and Optical Devices
Type
article
Field-Weighted Citation Impact
0.63

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article

Next-Value Prediction of Beam Waists From Mixed Pitch Grating Using Simplified Transformer Model

Chuan Seng Tan, L. Guidoni, J.-P. Likforman, Peng Zhao et al.
3 citations
IEEE photonics journal
Photonic and Optical Devices
0.63
article

Next-Value Prediction of Beam Waists From Mixed Pitch Grating Using Simplified Transformer Model

Chuan Seng Tan, L. Guidoni, J.-P. Likforman, Peng Zhao, Yu Dian Lim
article en
3 citations

Abstract

In this study, a simplified transformer model is used to predict the beam waist of 1,092 nm light coupled out from SiN-based mixed pitch gratings at various heights. The beam waists data at various heights above the grating is first compiled. Then, we used a sequence of the current beam waist values, z-positions, and the computed mathematical indicators (features) to predict the next beam waist value (labels). Optimized transformer model yields average percentage error (APE) of 6.6% between the predicted and actual beam waists, which corresponds to 93.4% prediction accuracy. This study provides a pioneering approach to using natural language processing model to perform predictive modelling on photonics data, and possible extrapolation of photonics data using transformer model.

IEEE photonics journalVol. 16(5)
Nanyang Technological University (SG), IMEC (BE), Laboratoire Matériaux et Phénomènes Quantiques (FR)
Ministry of Education, India
Openalex Percentile: Top 32%
Photonic and Optical Devices
0.63
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