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

doi:10.1117/12.2321355

5 September 2018 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

Author Affiliations +

Proceedings Volume 10728, Biosensing and Nanomedicine XI; 107280G (2018) https://doi.org/10.1117/12.2321355
Event: SPIE Nanoscience + Engineering, 2018, San Diego, California, United States

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.

Citation Download Citation

Patrick D. McAtee, Satish T. S. Bukkapatnam, and Akhlesh Lakhtakia "Artificial neural network to predict the refractive index of a liquid infiltrating a chiral sculptured thin film", Proc. SPIE 10728, Biosensing and Nanomedicine XI, 107280G (5 September 2018); https://doi.org/10.1117/12.2321355

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