Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle

被引:0
作者
Moon, Gwiyeong [1 ]
Son, Taehwang [1 ]
Lee, Hongki [1 ,2 ]
Kim, Donghyun [1 ]
机构
[1] Yonsei Univ, Sch Elect & Elect Engn, 50 Yonsei Ro, Seoul 03722, South Korea
[2] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
来源
PLASMONICS: DESIGN, MATERIALS, FABRICATION, CHARACTERIZATION, AND APPLICATIONS XX | 2022年 / 12197卷
基金
新加坡国家研究基金会;
关键词
surface plasmon scattering; surface plasmon resonance; deep learning; CLASSIFICATION; TRANSMISSION; MOBILE;
D O I
10.1117/12.2632959
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
We investigate the method to analyze interferometric plasmonic microscopy (IPM) images using a deep learning approach. An IPM image was generated by employing an optical model: the image intensity was formed by reflected and scattered fields. Convolutional neural network was utilized for the classification of IPM images. Conventional detection method based on fourier filtering was taken for comparison with the proposed method. It was confirmed that deep learning improves the performance significantly, in particular, robustness to noise. These results suggested applicability of deep learning beyond IPM images with higher efficiency.
引用
收藏
页数:7
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