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Performance optimization for plasmonic refractive index sensor based on machine learning
Author(s): Shuai Yu; Jia Wang; Tian Zhang; Ruilin Zhou; Jian Dai; Yue Zhou; Kun Xu
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Paper Abstract

In this article, we propose a novel method using machine learning, especially for artificial neural networks (ANNs) to achieve variability analysis and performance optimization of the plasmonic refractive index sensor (RIS). A Fano resonance (FR) based RIS which consisted of two plasmonic waveguides end-coupled to each other by an asymmetrical square resonator is taken as an illustration to demonstrate the effectiveness of the ANNs. The results reveal that the ANNs can be used in fast and accurate variability analysis because the predicted transmission spectrums and transmittances generated by ANNs are approximate to the actual simulated results. In addition, the ANNs can effectively solve the performance optimization and inverse design problems for the RIS by predicting the structure parameters for RIS accurately. Obviously, our proposed method has potential applications in optical sensing, device design, optical interconnects and so on.

Paper Details

Date Published: 14 February 2019
PDF: 5 pages
Proc. SPIE 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018), 110482X (14 February 2019); doi: 10.1117/12.2519699
Show Author Affiliations
Shuai Yu, Beijing Univ. of Posts and Telecommunications (China)
Jia Wang, Beijing Univ. of Posts and Telecommunications (China)
Tian Zhang, Beijing Univ. of Posts and Telecommunications (China)
Ruilin Zhou, Beijing Univ. of Posts and Telecommunications (China)
Jian Dai, Beijing Univ. of Posts and Telecommunications (China)
Yue Zhou, Beijing Univ. of Posts and Telecommunications (China)
Kun Xu, Beijing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 11048:
17th International Conference on Optical Communications and Networks (ICOCN2018)
Zhaohui Li, Editor(s)

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