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Machine learning and artificial neural networks for improved algorithmic design of nanophotonic structures

Published onJun 08, 2021
Machine learning and artificial neural networks for improved algorithmic design of nanophotonic structures
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Abstract

The study of the behavior of light at the nanometer scale is known as nanophotonics and it is the field that combines nanotechnology along with photonics. Over the past two decades, nanophotonics has been a promising and active research area, sparked by the growing interest in discovering new physics and technologies with light at the nanoscale. As the requirements on the level of integration and performance increase of nanophotonic applications, the design and optimization of nanostructures, with an immense number of possible combinations of features, for nanophotonic devices become time-inefficient and computationally expensive with the numerical simulations. The recent theoretical results show that machine learning (ML) and artificial neural network (ANN) techniques are capable of model nanophotonic structures for nanophotonic devices, at orders of magnitude lower time per result. It was a paradigm shift of research in nanophotonics to use ANNs as it has the most important advantages to use over existing traditional methods. Therefore, this project suggests utilizing ANN techniques to improve the algorithmic design of nanophotonic structures.

Article ID: 2021G10

Month: May

Year: 2021

Address: Online

Venue: Graduate Student Symposium- Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association

URL:https://caiac.pubpub.org/pub/koxvmvxn/

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