In this work, we explore the performance of plasmonic biosensor designs that integrate metamaterials based on machine learning algorithms. The meta-plasmonic biosensors were designed for optimized detection of DNA with a layer of double negative metamaterial modeled by an effective medium. An iterative transfer matrix approach was employed to generate training and test sets of resonance characteristics in the parameter space for machine learning. As a machine learning-based prediction of optical characteristics of a meta-plasmonic biosensor, multilayer perceptron and autoencoder (AE) were used as an algorithm, while the clustering algorithm was constructed by dimensional reduction based on AE and t-Stochastic Neighbor Embedding (t-SNE) as well as k-means clustering. Use of meta-plasmonic structure with analysis based on machine learning has found that enhancement of detection sensitivity by more than 13 times over conventional detection should be achievable with excellent reflectance curves. Further enhancement may be attained by expanding the parameter space.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation (NRF) grants ( NRF-2019R1A4A1025958 , 2019R1F1A1063602 , and 2019K2A9A2A08000198 ) funded by the Korean Government. Part of this work was performed on sabbatical at UC Irvine, supported by the National Science Foundation of the USA ( NSF CHE 1856165 ). Authors thank Prof. Kieron Burke at Department of Chemistry of UC Irvine for insightful discussion in this work.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering