Deep neural network for plasmonic sensor modeling

Abstract

Metallic plasmonic nanostructures have been widely used for ultra-sensitive, label-free and real-time chemical and biological molecule sensors. Computational modeling is the key for plasmonic sensor design and performance optimization, which relies on time-consuming electromagnetic simulations, and only the optimized result is useful while all other computation results are wasted. Deep learning method enabled by artificial neural networks provides a powerful and efficient tool to construct accurate correlation between plasmonic geometric parameters and resonance spectra. Without the need to run any costly simulations, the spectra of millions of different nanostructures can be obtained and the cost is only a one-time investment of two thousand groups of training data. This approach can be easily applied to other similar types of nanophotonic system which can help eliminate the simulation step and expedite the photonic sensor design process.