Identification of waveguide mode and surface plasmon resonance mode using Fourier cross-correlation analysis

Shen, Mengqi; Zhang, Bei; Wang, Qiusheng; Somekh, Michael Geoffrey; Li, Ang

doi:10.1364/ao.58.006920

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Therefore, the measurement precision depends on the accuracy of a plasmonic dip position measurement under the noise constraint. Several minimum reflectance dip determination methods have been proposed and developed, including polynomial curve fitting 18 , 19 , cross correlation 20 , and centroid determination 21 .…”

Section: Introductionmentioning

confidence: 99%

Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning

Thadson

Sasivimolkul

Suvarnaphaet

et al. 2022

Sci Rep

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Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10–5 RIU to 10–6 RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10–6 RIU for the proposed artificial intelligence-based method compared to 7.03 × 10–6 RIU for the cubic polynomial curve fitting and 5.59 × 10–6 RIU for 2-dimensional contour fitting using Horner's method.

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