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

## TL;DR

This paper demonstrates how artificial neural networks can accurately estimate the refractive index of liquids infiltrating a chiral sculptured thin film by analyzing optical reflectance data, enhancing optical sensing capabilities.

## Contribution

It introduces a neural network-based method for predicting infiltrant refractive index from optical data, showing robustness with realistic noise and favoring simpler network structures.

## Key findings

- Neural networks can accurately predict refractive index from simulated optical data.
- Simpler neural network structures perform best in this application.
- The method remains robust under realistic noise conditions.

## Abstract

We theoretically expanded the capabilities of optical sensing based on surface plasmon resonance in a prism-coupled configuration by incorporating artificial neural networks (ANNs). We used calculations modeling the situation in which an index-matched substrate with a metal thin film and a porous chiral sculptured thin film (CSTF) deposited successively on it is 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 two ANNs with differing structures using reflectance data generated from simulations to predict the refractive index of the infiltrant fluid. The best predictions were a result of training the ANN with simpler structure. With realistic simulated-noise, the performance of this ANN is robust.