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Artificial neural network to estimate the refractive index of a liquid infiltrating a chiral sculptured thin film in a sensor chip
Author(s): Patrick D. McAtee; Satish T. S. Bukkapatnam; Akhlesh Lakhtakia
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Paper Abstract

We experimentally expanded the capabilities of optical sensing based on surface plasmon resonance in a prism- coupled configuration by incorporating artificial neural networks (ANNs). We fabricated a sensor chip comprising a metal thin film and a porous chiral sculptured thin film (CSTF) deposited successively on a glass substrate that can be 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 an ANN using measured reflectance data and found that the presence of the CSTF does not inhibit sensing performance. This finding clears the way for further research on using a single sensor chip for simultaneous multi-analyte sensing.

Paper Details

Date Published: 6 December 2019
PDF: 7 pages
Proc. SPIE 11371, International Workshop on Thin Films for Electronics, Electro-Optics, Energy, and Sensors 2019, 1137103 (6 December 2019); doi: 10.1117/12.2530355
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. 11371:
International Workshop on Thin Films for Electronics, Electro-Optics, Energy, and Sensors 2019
Partha Banerjee; Karl Gudmundsson; Akhlesh Lakhtakia; Guru Subramanyam, Editor(s)

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