Share Email Print

Proceedings Paper

Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film
Author(s): Patrick D. McAtee; Satish T. S. Bukkapatnam; Akhlesh Lakhtakia
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 5 September 2018
PDF: 13 pages
Proc. SPIE 10728, Biosensing and Nanomedicine XI, 107280G (5 September 2018); doi: 10.1117/12.2321355
Show Author Affiliations
Patrick D. McAtee, The Pennsylvania State Univ. (United States)
Satish T. S. Bukkapatnam, Texas A&M Univ. (United States)
Akhlesh Lakhtakia, The Pennsylvania State Univ. (United States)

Published in SPIE Proceedings Vol. 10728:
Biosensing and Nanomedicine XI
Hooman Mohseni; Massoud H. Agahi; Manijeh Razeghi, Editor(s)

© SPIE. Terms of Use
Back to Top