Machine learning improves the way to design meta-plasmonic biosensors

Gwiyeong Moon, Jong Ryul Choi, Changhun Lee, Youngjin Oh, Kyung Hwan Kim, Donghyun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this work, we explore a way to design meta-plasmonic structure-based biosensors using machine learning methods. Plasmonic biosensing is a label-free detection method that is widely used to measure various biomolecular interactions. One of the main challenges has been how to improve the sensitivity and detection limit to detect very small molecules at low concentrations. Here, metamaterial was employed to address these issues using machine learning for the design. Transfer matrix algorithm was used to calculate optical characteristics of meta-plasmonic structure to generate training data. The multilayer perceptron was then applied to predict the optical characteristics of the meta-plasmonic structure. The performance was compared with conventional interpolation methods. Multilayer perceptron was shown to achieve mean squared error lower by about 1.5 times. Autoencoder and t-Stochastic Neighbor Embedding were also used to cluster the optical characteristics. Structural parameters which provide resonance in reflection can be found through clustering of optical characteristics. It was shown that meta-plasmonic structure improves sensitivity by more than ten times over conventional plasmonic biosensors. We expect that machine learning methods can be further extended to other biosensing modalities.

Original languageEnglish
Title of host publicationPlasmonics in Biology and Medicine XVIII
EditorsTuan Vo-Dinh, Ho-Pui A. Ho, Krishanu Ray
ISBN (Electronic)9781510641570
Publication statusPublished - 2021
EventPlasmonics in Biology and Medicine XVIII 2021 - Virtual, Online, United States
Duration: 2021 Mar 62021 Mar 11

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferencePlasmonics in Biology and Medicine XVIII 2021
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 SPIE. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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