Abstract
The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational 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 separation gap, 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 been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis 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 article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.
Highlights
Nanostructures have recently gained a lot of attention from researchers because of their diverse applications, and the global market value of nanotechnology is predicted to reach USD 90 billion by 2021 [1] as consumer and industrial applications of nanostructures are rising continuously [2]
Was considered as the third important output parameter, which was defined as the ratio of the sensitivity to the Full Width Half Maximum (FWHM), i.e., Figure of Merit (FOM) = S(nm RIU−1 )/FW HM, and the plasmonic wavelength was considered as the fourth output parameter as it tells about the maximum relative response amplitude at particular wavelengths [35,36] to collect the dataset for neural network training
This paper demonstrates the crucial steps for rigorous testing of the artificial neural network and made good predictions with a trained network
Summary
Nanostructures have recently gained a lot of attention from researchers because of their diverse applications, and the global market value of nanotechnology is predicted to reach USD 90 billion by 2021 [1] as consumer and industrial applications of nanostructures are rising continuously [2]. Artificial Intelligence (AI) has seen rapid growth in the last decade [3] and being adopted by computer scientists and specialists and by other researchers in various fields It has shown widespread popularity in handling complex data-driven problems in science and technology [4]. This developed model can provide output parameters using any new input values after sufficient training This learning algorithm can compare its predicted output with the actual output values and calculate the mean squared errors to show the accuracy of the designed model. In this deep neural network, many popular ML frameworks have been used while developing and training the network, such as pandas [25]. This research creates the opportunity to calculate the optical parameters for paired nanostructural devices by using artificial neural network optimization methods
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