Modeling and predicting the beam properties from grating structures using deep neural network

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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Main Authors: Lim, Yu Dian, Zhao, Peng, Guidoni, Luca, Likforman, Jean-Pierre, Tan, Chuan Seng
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
Engineering
Particle beam optics
Trapped ions
Online Access:https://hdl.handle.net/10356/175559
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Institution: Nanyang Technological University
Language: English
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spelling 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.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Particle beam optics
Trapped ions
spellingShingle 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

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