Artificial intelligence-aided nanoplasmonic biosensor modeling

Abstract

The use of monolithic biosensors is increasingly popular in fundamental biological studies. Rapid advances in nanophotonic biosensors are leading to lab-on-a-chip platforms. In this paper we propose a method to use artificial neural networks (ANNs) to predict the output electrical signal of biosensor. Multilayer perceptron model is developed by assuming electrical current as outputs, and the refractive index of biosamples, central wavelength (λ) and full width half maximum (FWHM) of input light source as inputs. A comparative approach was applied between finite-difference time-domain (FDTD) method and ANN results to evaluate the biosensor’s ANN model. Results showed that the ANN design with topology of (3 5 4 4 6 21) can predict the output accurately based on the value of mean square error (MSE) about 2.9 × 10−8 as evaluation parameter. It was shown that the developed ANN model can approximate the outcome to high precision with only a small sampling of the data. Using the developed model, pre-optimization was run to find the optimum condition for electrical sensitivity and responsivity of the device. We found that the light source with central wavelength of 735 nm and FWHM of 70 nm can simultaneously satisfy the optimum conditions for sensitivity and responsivity.