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Lookup NU author(s): Dr Sneha Verma
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The Artificial Neural Network (ANN) has become an attractive approach in MachineLearning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it hasbecame popular in many scientific fields such as physics, optics, and material science. This paperpresents a new approach to design and optimize the electromagnetic plasmonic nanostructuresusing a computationally efficient method based on the ANN. In this work, the nanostructures havebeen simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is usedfor making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figureof Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, thecomputational model is developed by using a Finite Element Method (FEM) to prepare the dataset.The input parameters were considered as the Major axis, a, the Minor axis, b, and the separationgap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm),FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has beendesigned where the number of hidden layers and neurons were optimized as part of a comprehensiveanalysis to improve the efficiency of ML model. After successfully optimizing the neural network,this model is used to make predictions for specific inputs and its corresponding outputs. This articlealso compares the error between the predicted and simulated results. This approach outperforms thedirect numerical simulation methods for predicting output for various input device parameters.
Author(s): Verma S, Chugh S, Gosh S, Rahman BMA
Publication type: Article
Publication status: Published
Journal: Nanomaterials
Year: 2022
Volume: 12
Issue: 1
Online publication date: 04/01/2022
Acceptance date: 29/12/2021
Date deposited: 30/09/2024
ISSN (electronic): 2079-4991
Publisher: MDPI AG
URL: https://doi.org/10.3390/nano12010170
DOI: 10.3390/nano12010170
Data Access Statement: The data is available on reasonable request from the corresponding author.
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