Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach

Yi Ren; Menglin Zhai; Rui Pei; Peng Wang

doi:10.1109/ACES-China56081.2022.10064754

Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach

Yi Ren, Menglin Zhai^*, Rui Pei, Peng Wang

^*Corresponding author for this work

Donghua University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This paper employs an attention based convolutional neural network long short-term memory (A-CNN-LSTM) approach for the prediction of metasurface transmission spectrum. By inputting the pixel map of the metasurface structure, the trained A-CNN-LSTM model can quickly output corresponding transmission spectral data. The experimental results for different metasurface structures show that the A-CNN-LSTM has higher accuracy compared with the traditional CNN and CNN-LSTM models. And they agree well with traditional FDTD software simulation but can be much faster.

Original language	English
Title of host publication	2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665452366
DOIs	https://doi.org/10.1109/ACES-China56081.2022.10064754
Publication status	Published - 2022
Externally published	Yes
Event	2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 - Xuzhou, China Duration: 9 Dec 2022 → 12 Dec 2022

Publication series

Name	2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

Conference

Conference	2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022
Country/Territory	China
City	Xuzhou
Period	9/12/22 → 12/12/22

Keywords

A-CNN-LSTM
metasurface
transmission spectrum

Access to Document

10.1109/ACES-China56081.2022.10064754

Cite this

Ren, Y., Zhai, M., Pei, R., & Wang, P. (2022). Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach. In 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 (2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACES-China56081.2022.10064754

Ren, Yi ; Zhai, Menglin ; Pei, Rui et al. / Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach. 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022).

@inproceedings{a21412095fa3483b9992b197bdaca9c4,

title = "Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach",

abstract = "This paper employs an attention based convolutional neural network long short-term memory (A-CNN-LSTM) approach for the prediction of metasurface transmission spectrum. By inputting the pixel map of the metasurface structure, the trained A-CNN-LSTM model can quickly output corresponding transmission spectral data. The experimental results for different metasurface structures show that the A-CNN-LSTM has higher accuracy compared with the traditional CNN and CNN-LSTM models. And they agree well with traditional FDTD software simulation but can be much faster.",

keywords = "A-CNN-LSTM, metasurface, transmission spectrum",

author = "Yi Ren and Menglin Zhai and Rui Pei and Peng Wang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022 ; Conference date: 09-12-2022 Through 12-12-2022",

year = "2022",

doi = "10.1109/ACES-China56081.2022.10064754",

language = "English",

series = "2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022",

}

Ren, Y, Zhai, M, Pei, R & Wang, P 2022, Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach. in 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022. 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022, Xuzhou, China, 9/12/22. https://doi.org/10.1109/ACES-China56081.2022.10064754

Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach. / Ren, Yi; Zhai, Menglin; Pei, Rui et al.
2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach

AU - Ren, Yi

AU - Zhai, Menglin

AU - Pei, Rui

AU - Wang, Peng

PY - 2022

Y1 - 2022

N2 - This paper employs an attention based convolutional neural network long short-term memory (A-CNN-LSTM) approach for the prediction of metasurface transmission spectrum. By inputting the pixel map of the metasurface structure, the trained A-CNN-LSTM model can quickly output corresponding transmission spectral data. The experimental results for different metasurface structures show that the A-CNN-LSTM has higher accuracy compared with the traditional CNN and CNN-LSTM models. And they agree well with traditional FDTD software simulation but can be much faster.

AB - This paper employs an attention based convolutional neural network long short-term memory (A-CNN-LSTM) approach for the prediction of metasurface transmission spectrum. By inputting the pixel map of the metasurface structure, the trained A-CNN-LSTM model can quickly output corresponding transmission spectral data. The experimental results for different metasurface structures show that the A-CNN-LSTM has higher accuracy compared with the traditional CNN and CNN-LSTM models. And they agree well with traditional FDTD software simulation but can be much faster.

KW - A-CNN-LSTM

KW - metasurface

KW - transmission spectrum

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U2 - 10.1109/ACES-China56081.2022.10064754

DO - 10.1109/ACES-China56081.2022.10064754

M3 - Conference Proceeding

AN - SCOPUS:85151553932

T3 - 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

BT - 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

Y2 - 9 December 2022 through 12 December 2022

ER -

Ren Y, Zhai M, Pei R, Wang P. Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach. In 2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022). doi: 10.1109/ACES-China56081.2022.10064754

Prediction of Metasurface Transmission Spectrum Based on An A-CNN-LSTM Approach

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