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

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

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

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.

Original languageEnglish (US)
Title of host publicationInternational Workshop on Thin Films for Electronics, Electro-Optics, Energy, and Sensors 2019
EditorsPartha Banerjee, Karl Gudmundsson, Akhlesh Lakhtakia, Guru Subramanyam
PublisherSPIE
ISBN (Electronic)9781510635135
DOIs
StatePublished - 2019
EventInternational Workshop on Thin Films for Electronics, Electro-Optics, Energy, and Sensors 2019, TFE3S 2019 - Reykjavik, Iceland
Duration: Jun 24 2019Jun 26 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11371
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Workshop on Thin Films for Electronics, Electro-Optics, Energy, and Sensors 2019, TFE3S 2019
CountryIceland
CityReykjavik
Period6/24/196/26/19

All Science Journal Classification (ASJC) codes

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

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  • Cite this

    McAtee, P. D., Bukkapatnam, S. T. S., & Lakhtakia, A. (2019). Artificial neural network to estimate the refractive index of a liquid infiltrating a chiral sculptured thin film in a sensor chip. In P. Banerjee, K. Gudmundsson, A. Lakhtakia, & G. Subramanyam (Eds.), International Workshop on Thin Films for Electronics, Electro-Optics, Energy, and Sensors 2019 [1137103] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11371). SPIE. https://doi.org/10.1117/12.2530355