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

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

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

Abstract

We expanded the capabilities of surface multiplasmonic resonance sensing via a prism-coupled configuration by devising a new scheme to analyze data obtained from simulations and/or experiments. An index-matched substrate with a metal thin film and a chiral sculptured thin film (CSTF) deposited successively on it is affixed to the base of a prism with an isosceles triangle as its cross section. 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 an artificial neural network (ANN) using reflectance data generated from simulations to predict the refractive index of the infiltrant fluid. ANN performance for various incidence conditions was studied. The scheme is quite robust.

Original languageEnglish (US)
Title of host publicationBiosensing and Nanomedicine XI
EditorsMassoud H. Agahi, Manijeh Razeghi, Hooma Mohseni, Massoud H. Agahi
PublisherSPIE
Volume10728
ISBN (Print)9781510620278
DOIs
StatePublished - Jan 1 2018
EventBiosensing and Nanomedicine XI 2018 - San Diego, United States
Duration: Aug 19 2018Aug 20 2018

Other

OtherBiosensing and Nanomedicine XI 2018
CountryUnited States
CitySan Diego
Period8/19/188/20/18

Fingerprint

Prism
Prisms
Refractive Index
prisms
Artificial Neural Network
Thin Films
Refractive index
Face
Liquid
refractivity
Neural networks
Predict
Thin films
Liquids
liquids
thin films
Isosceles triangle
Fluid
Infiltration
Network Performance

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

McAtee, P. D., Bukkapatnam, S. T. S., & Lakhtakia, A. (2018). Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film. In M. H. Agahi, M. Razeghi, H. Mohseni, & M. H. Agahi (Eds.), Biosensing and Nanomedicine XI (Vol. 10728). [107280G] SPIE. https://doi.org/10.1117/12.2321355
McAtee, Patrick D. ; Bukkapatnam, Satish T.S. ; Lakhtakia, Akhlesh. / Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film. Biosensing and Nanomedicine XI. editor / Massoud H. Agahi ; Manijeh Razeghi ; Hooma Mohseni ; Massoud H. Agahi. Vol. 10728 SPIE, 2018.
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McAtee, PD, Bukkapatnam, STS & Lakhtakia, A 2018, Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film. in MH Agahi, M Razeghi, H Mohseni & MH Agahi (eds), Biosensing and Nanomedicine XI. vol. 10728, 107280G, SPIE, Biosensing and Nanomedicine XI 2018, San Diego, United States, 8/19/18. https://doi.org/10.1117/12.2321355

Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film. / McAtee, Patrick D.; Bukkapatnam, Satish T.S.; Lakhtakia, Akhlesh.

Biosensing and Nanomedicine XI. ed. / Massoud H. Agahi; Manijeh Razeghi; Hooma Mohseni; Massoud H. Agahi. Vol. 10728 SPIE, 2018. 107280G.

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

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McAtee PD, Bukkapatnam STS, Lakhtakia A. Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film. In Agahi MH, Razeghi M, Mohseni H, Agahi MH, editors, Biosensing and Nanomedicine XI. Vol. 10728. SPIE. 2018. 107280G https://doi.org/10.1117/12.2321355