Modeling and optimization of nano-rod plasmonic sensor by adaptive neuro fuzzy inference system (ANFIS)

Abstract

Predicting the behavior of photonic and Plasmon nanostructures has always been an attractive topic for photonic sciences researchers. One of the methods for estimating the behavior of devices in engineering sciences is adaptive neuro fuzzy inference system. Modeling and designing of photonic and plasmonic nanostructures and their optimization strongly relies on the timing of unstructured electromagnetic response simulations and optimal design requires a high number of simulations and if the optimal answer is reached, other simulation results will be wasted. Deep learning method which has been created by ANFIS networks provides a powerful and efficient device for creating accurate relationship between plasmonic geometric parameters and resonance spectrums. Millions of different nanostructures can be obtained without the need for any costly simulations and it costs only one investment to obtain training data. In this study, we use of deep learning based on ANFIS for predicting optic sensors’ answer by using plasmonic nano rods. In addition to prediction, this method can approximate a special answer based on photonic structure. In this study, ANFIS networks, at first, model a 5*5 array of plasmonic nanostructures based on obtained experimental data from optic absorption. In the following, based on obtained model, it designs nanoparticles geometry so that we achieve a relatively narrow absorption spectrum with high sensitivity in relation to change air refractive index. This method can be applied for other similar types of nano-photonic systems which can help to destroy simulation procedure and acceleration photonic sensor design trend.