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Deep neural network-based phase calibration in integrated optical phased arrays
Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamformin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651891/ https://www.ncbi.nlm.nih.gov/pubmed/37968312 http://dx.doi.org/10.1038/s41598-023-47004-z |
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author | Kim, Jae-Yong Kim, Junhyeong Yoon, Jinhyeong Hong, Seokjin Neseli, Berkay Kwon, Namhyun You, Jong-Bum Yoon, Hyeonho Park, Hyo-Hoon Kurt, Hamza |
author_facet | Kim, Jae-Yong Kim, Junhyeong Yoon, Jinhyeong Hong, Seokjin Neseli, Berkay Kwon, Namhyun You, Jong-Bum Yoon, Hyeonho Park, Hyo-Hoon Kurt, Hamza |
author_sort | Kim, Jae-Yong |
collection | PubMed |
description | Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming in integrated OPAs. Our methodology focuses on precise phase control, individually tailored to each of the 64 OPA channels, incorporating electro-optic phase shifters. To effectively handle the inherent complexity arising from the numerous voltage set combinations required for phase control across the 64 channels, we employ a tandem network architecture, further optimizing it through selective data sorting and hyperparameter tuning. To validate the effectiveness of the trained DNN model, we compared its performance with 20 reference beams obtained through the hill climbing algorithm. Despite an average intensity reduction of 0.84 dB in the peak values of the beams compared to the reference beams, our experimental results demonstrate substantial agreements between the DNN-predicted beams and the reference beams, accompanied by a slight decrease of 0.06 dB in the side-mode-suppression-ratio. These results underscore the practical effectiveness of the DNN model in OPA beamforming, highlighting its potential in scenarios that necessitate the intelligent and time-efficient calibration of multiple beams. |
format | Online Article Text |
id | pubmed-10651891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106518912023-11-15 Deep neural network-based phase calibration in integrated optical phased arrays Kim, Jae-Yong Kim, Junhyeong Yoon, Jinhyeong Hong, Seokjin Neseli, Berkay Kwon, Namhyun You, Jong-Bum Yoon, Hyeonho Park, Hyo-Hoon Kurt, Hamza Sci Rep Article Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming in integrated OPAs. Our methodology focuses on precise phase control, individually tailored to each of the 64 OPA channels, incorporating electro-optic phase shifters. To effectively handle the inherent complexity arising from the numerous voltage set combinations required for phase control across the 64 channels, we employ a tandem network architecture, further optimizing it through selective data sorting and hyperparameter tuning. To validate the effectiveness of the trained DNN model, we compared its performance with 20 reference beams obtained through the hill climbing algorithm. Despite an average intensity reduction of 0.84 dB in the peak values of the beams compared to the reference beams, our experimental results demonstrate substantial agreements between the DNN-predicted beams and the reference beams, accompanied by a slight decrease of 0.06 dB in the side-mode-suppression-ratio. These results underscore the practical effectiveness of the DNN model in OPA beamforming, highlighting its potential in scenarios that necessitate the intelligent and time-efficient calibration of multiple beams. Nature Publishing Group UK 2023-11-15 /pmc/articles/PMC10651891/ /pubmed/37968312 http://dx.doi.org/10.1038/s41598-023-47004-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kim, Jae-Yong Kim, Junhyeong Yoon, Jinhyeong Hong, Seokjin Neseli, Berkay Kwon, Namhyun You, Jong-Bum Yoon, Hyeonho Park, Hyo-Hoon Kurt, Hamza Deep neural network-based phase calibration in integrated optical phased arrays |
title | Deep neural network-based phase calibration in integrated optical phased arrays |
title_full | Deep neural network-based phase calibration in integrated optical phased arrays |
title_fullStr | Deep neural network-based phase calibration in integrated optical phased arrays |
title_full_unstemmed | Deep neural network-based phase calibration in integrated optical phased arrays |
title_short | Deep neural network-based phase calibration in integrated optical phased arrays |
title_sort | deep neural network-based phase calibration in integrated optical phased arrays |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651891/ https://www.ncbi.nlm.nih.gov/pubmed/37968312 http://dx.doi.org/10.1038/s41598-023-47004-z |
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