  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.
