Artificial neural network to estimate the refractive index of a liquid infiltrating a chiral sculptured thin film

Patrick D. McAtee, Satish T.S. Bukkapatnam, Akhlesh Lakhtakia

Research output: Contribution to journalArticle

Abstract

We theoretically expanded the capabilities of optical sensing based on surface plasmon resonance in a prism-coupled configuration by incorporating artificial neural networks (ANNs). We used calculations modeling an index-matched substrate with a metal thin film and a porous chiral sculptured thin film (CSTF) deposited successively on it that is affixed to the base of a triangular prism. When a fluid is brought in contact with the exposed face of the CSTF, the latter is infiltrated. As a result of infiltration, the traversal of light entering one slanted face of the prism and exiting the other slanted face of the prism is affected.We trained two ANNs with differing structures using reflectance data generated from simulations to predict the refractive index of the infiltrant fluid. The best predictions were a result of training the ANN with the simpler structure. With realistic simulated-noise, the performance of this ANN is robust.

Original languageEnglish (US)
Article number046006
JournalJournal of Nanophotonics
Volume13
Issue number4
DOIs
StatePublished - Oct 1 2019

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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