Photonic Crystal Cavity Optimization Using AI

Diptimayee Dash; Jasmine Saini; Amit Kumar Goyal

doi:10.1109/ICSC53193.2021.9673247

Photonic Crystal Cavity Optimization Using AI

Diptimayee Dash, Jasmine Saini, Amit Kumar Goyal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this manuscript, photonic crystal cavity optimization using artificial intelligence has been done. The proposed structure is very simple and has high sensitivity. Porosity in the layers has been intentionally added for ease of analyte infiltrate, which will increase the sensitivity of the crystal but reduces the strength of the transmitted signal. The structure is designed considering alternate layers of high and low refractive index material. For this, SiO2 and MgF2 materials are considered. The mean error is minimized by considering optimum value of 'k' as 4. Different algorithms were checked on the dataset and finally the kNN algorithm has been applied on the dataset and F1-score of about 0.97 is obtained.

Original language	English (US)
Title of host publication	2021 7th International Conference on Signal Processing and Communication, ICSC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	111-113
Number of pages	3
ISBN (Print)	9781665427395
DOIs	https://doi.org/10.1109/ICSC53193.2021.9673247
State	Published - Jan 1 2021
Externally published	Yes

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10.1109/ICSC53193.2021.9673247

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@inproceedings{11f6ccb699ca417d9742ba320a4602f9,

title = "Photonic Crystal Cavity Optimization Using AI",

abstract = "In this manuscript, photonic crystal cavity optimization using artificial intelligence has been done. The proposed structure is very simple and has high sensitivity. Porosity in the layers has been intentionally added for ease of analyte infiltrate, which will increase the sensitivity of the crystal but reduces the strength of the transmitted signal. The structure is designed considering alternate layers of high and low refractive index material. For this, SiO2 and MgF2 materials are considered. The mean error is minimized by considering optimum value of 'k' as 4. Different algorithms were checked on the dataset and finally the kNN algorithm has been applied on the dataset and F1-score of about 0.97 is obtained.",

author = "Diptimayee Dash and Jasmine Saini and Goyal, {Amit Kumar}",

note = "Generated from Scopus record by KAUST IRTS on 2023-10-12",

year = "2021",

month = jan,

day = "1",

doi = "10.1109/ICSC53193.2021.9673247",

language = "English (US)",

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booktitle = "2021 7th International Conference on Signal Processing and Communication, ICSC 2021",

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

address = "United States",

}

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AU - Dash, Diptimayee

AU - Saini, Jasmine

AU - Goyal, Amit Kumar

N1 - Generated from Scopus record by KAUST IRTS on 2023-10-12

PY - 2021/1/1

Y1 - 2021/1/1

N2 - In this manuscript, photonic crystal cavity optimization using artificial intelligence has been done. The proposed structure is very simple and has high sensitivity. Porosity in the layers has been intentionally added for ease of analyte infiltrate, which will increase the sensitivity of the crystal but reduces the strength of the transmitted signal. The structure is designed considering alternate layers of high and low refractive index material. For this, SiO2 and MgF2 materials are considered. The mean error is minimized by considering optimum value of 'k' as 4. Different algorithms were checked on the dataset and finally the kNN algorithm has been applied on the dataset and F1-score of about 0.97 is obtained.

AB - In this manuscript, photonic crystal cavity optimization using artificial intelligence has been done. The proposed structure is very simple and has high sensitivity. Porosity in the layers has been intentionally added for ease of analyte infiltrate, which will increase the sensitivity of the crystal but reduces the strength of the transmitted signal. The structure is designed considering alternate layers of high and low refractive index material. For this, SiO2 and MgF2 materials are considered. The mean error is minimized by considering optimum value of 'k' as 4. Different algorithms were checked on the dataset and finally the kNN algorithm has been applied on the dataset and F1-score of about 0.97 is obtained.

UR - https://ieeexplore.ieee.org/document/9673247/

UR - http://www.scopus.com/inward/record.url?scp=85125108463&partnerID=8YFLogxK

U2 - 10.1109/ICSC53193.2021.9673247

DO - 10.1109/ICSC53193.2021.9673247

M3 - Conference contribution

SN - 9781665427395

SP - 111

EP - 113

BT - 2021 7th International Conference on Signal Processing and Communication, ICSC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Photonic Crystal Cavity Optimization Using AI

Abstract

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