Modeling and predicting the beam properties from grating structures using deep neural network
In this study, the optical properties of light beam coupled out from grating structures are simulated, modeled, and predicted. Gratings with various radius of curvature (R) are designed, where optical properties of light coupled out from the grating are modeled with deep neural network (DNN). After...
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sg-ntu-dr.10356-1755592024-04-30T05:51:05Z Modeling and predicting the beam properties from grating structures using deep neural network Lim, Yu Dian Zhao, Peng Guidoni, Luca Likforman, Jean-Pierre Tan, Chuan Seng School of Electrical and Electronic Engineering 2023 18th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT) Institute of Microelectronics, A*STAR Engineering Particle beam optics Trapped ions In this study, the optical properties of light beam coupled out from grating structures are simulated, modeled, and predicted. Gratings with various radius of curvature (R) are designed, where optical properties of light coupled out from the grating are modeled with deep neural network (DNN). After omitting the underfitted and overfitted model, the ideally-fitted model accurately predicts the beam waist of light from R = 30 μm with average percentage error (APE) of 7.3%. At the same time, the ideally-fitted model exhibits training/validation loss APE of 2.2%, indicating that the model is well-fitted. National Research Foundation (NRF) This work was supported by ANR-NRF Joint Grant Call under Grant NRF2020-NRF-ANR073 HIT. 2024-04-30T05:51:05Z 2024-04-30T05:51:05Z 2023 Conference Paper Lim, Y. D., Zhao, P., Guidoni, L., Likforman, J. & Tan, C. S. (2023). Modeling and predicting the beam properties from grating structures using deep neural network. 2023 18th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), 133-136. https://dx.doi.org/10.1109/IMPACT59481.2023.10348767 9798350384123 2150-5942 https://hdl.handle.net/10356/175559 10.1109/IMPACT59481.2023.10348767 2-s2.0-85182741430 133 136 en NRF2020-NRF-ANR073 HIT © 2023 Elsevier. All rights reserved. |
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Engineering Particle beam optics Trapped ions |
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Engineering Particle beam optics Trapped ions Lim, Yu Dian Zhao, Peng Guidoni, Luca Likforman, Jean-Pierre Tan, Chuan Seng Modeling and predicting the beam properties from grating structures using deep neural network |
| description |
In this study, the optical properties of light beam coupled out from grating structures are simulated, modeled, and predicted. Gratings with various radius of curvature (R) are designed, where optical properties of light coupled out from the grating are modeled with deep neural network (DNN). After omitting the underfitted and overfitted model, the ideally-fitted model accurately predicts the beam waist of light from R = 30 μm with average percentage error (APE) of 7.3%. At the same time, the ideally-fitted model exhibits training/validation loss APE of 2.2%, indicating that the model is well-fitted. |
| author2 |
School of Electrical and Electronic Engineering |
| author_facet |
School of Electrical and Electronic Engineering Lim, Yu Dian Zhao, Peng Guidoni, Luca Likforman, Jean-Pierre Tan, Chuan Seng |
| format |
Conference or Workshop Item |
| author |
Lim, Yu Dian Zhao, Peng Guidoni, Luca Likforman, Jean-Pierre Tan, Chuan Seng |
| author_sort |
Lim, Yu Dian |
| title |
Modeling and predicting the beam properties from grating structures using deep neural network |
| title_short |
Modeling and predicting the beam properties from grating structures using deep neural network |
| title_full |
Modeling and predicting the beam properties from grating structures using deep neural network |
| title_fullStr |
Modeling and predicting the beam properties from grating structures using deep neural network |
| title_full_unstemmed |
Modeling and predicting the beam properties from grating structures using deep neural network |
| title_sort |
modeling and predicting the beam properties from grating structures using deep neural network |
| publishDate |
2024 |
| url |
https://hdl.handle.net/10356/175559 |
| _version_ |
1814047335801421824 |