Demonstration of graphene-assisted tunable surface plasmonic resonance sensor using machine learning model

This work illustrates the viability of optics ideas using a machine learning (ML) technique to choose the optimal SPR sensor for a particular set of structural parameters. Particle swarm optimization (PSO) algorithm is utilized in conjunction with an ML model to design a tunable surface plasmonic resonance (SPR) sensor. A trained ML model is applied to the PSO algorithm to develop the SPR sensor with the desired sensing performance. Using a learned ML model to forecast sensor performance rather than sophisticated electromagnetic calculation techniques allows the PSO algorithm to optimize solutions faster with four orders of magnitude. This composite algorithm's implementation enabled us to rapidly and precisely create an SPR sensor with a sensitivity of 68.754 degrees/RIU and having an impressive figure of merit of 100. We anticipate that the proposed effective and precise method will pave the way for the future development of plasmonic devices.

Automated summary

This study demonstrates the feasibility of using a machine learning technique to select the optimal surface plasmonic resonance (SPR) sensor based on specific structural parameters. The particle swarm optimization (PSO) algorithm and a trained ML model are utilized to design a tunable SPR sensor with desired sensing performance. By using a learned ML model to forecast sensor performance, the PSO algorithm can optimize solutions faster, with a speed improvement of four orders of magnitude. The implementation of this composite algorithm enables the rapid and precise creation of an SPR sensor with a sensitivity of 68.754 degrees/RIU and an impressive figure of merit of 100. It is anticipated that this effective and precise method will contribute to the future development of plasmonic devices.