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Optimization of colour generation from dielectric nanostructures using reinforcement learning SCIE SCOPUS

Title
Optimization of colour generation from dielectric nanostructures using reinforcement learning
Authors
SAJEDIAN, IMANTREVON, BADLOERHO, JUNSUK
Date Issued
2019-02
Publisher
OPTICAL SOC AMER
Abstract
Recently, a novel machine learning model has emerged in the field of reinforcement learning known as deep Q-learning. This model is capable of finding the best possible solution in systems consisting of millions of choices, without ever experiencing it before, and has been used to beat the best human minds at complex games such as, Go and chess, which both have a huge number of possible decisions and outcomes for each move. With a human-level intelligence, it has solved the problems that no other machine learning model has done before. Here, we show the steps needed for implementing this model to an optical problem. We investigate the colour generation by dielectric nanostructures and show that this model can find geometrical properties that can generate much purer red, green and blue colours compared to previously reported results. The model found these results in 9000 steps from a possible 34.5 million solutions. This technique can easily be extended to predict and optimise the design parameters for other optical structures. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
URI
https://oasis.postech.ac.kr/handle/2014.oak/94909
DOI
10.1364/OE.27.005874
ISSN
1094-4087
Article Type
Article
Citation
OPTICS EXPRESS, vol. 27, no. 4, page. 5874 - 5883, 2019-02
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노준석RHO, JUNSUK
Dept of Mechanical Enginrg
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