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

Gwiyeong Moon; Taehwang Son; Hongki Lee; Donghyun Kim

doi:10.1117/12.2632959

3 October 2022 Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle

Gwiyeong Moon, Taehwang Son, Hongki Lee, Donghyun Kim

Author Affiliations +

Proceedings Volume 12197, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX; 121970B (2022) https://doi.org/10.1117/12.2632959
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

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Gwiyeong Moon, Taehwang Son, Hongki Lee, and Donghyun Kim "Deep learning based approach on interferometric plasmonic microscopy images for efficient detection of nanoparticle", Proc. SPIE 12197, Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XX, 121970B (3 October 2022); https://doi.org/10.1117/12.2632959

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