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

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Abstract

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 °/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.

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Acknowledgements

Anuj K. Sharma and Yogendra Kumar Prajapati gratefully acknowledge the core research grant (Project no. CRG/2019/002636) from the Science and Engineering Research Board (SERB) India that sponsored this research work.

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No funds, grants or other support were received.

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Kushagra Rastogi & Yogendra Kumar Prajapati

  2. Physics Division, Department of Applied Sciences, National Institute of Technology Delhi, GT Karnal Road, Delhi, 110036, India

    Anuj Kumar Sharma

Authors
  1. Kushagra Rastogi
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Contributions

KR and YKP contributed to design, methodology, formal analysis, investigation, validation and writing—original draft preparation; AKS contributed to conceptualization, resources, supervision and writing—review and editing.

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Correspondence to Yogendra Kumar Prajapati.

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Rastogi, K., Sharma, A.K. & Prajapati, Y.K. Demonstration of graphene-assisted tunable surface plasmonic resonance sensor using machine learning model. Appl. Phys. A 129, 351 (2023). https://doi.org/10.1007/s00339-023-06630-0

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