Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

Abstract

A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection

“…A theoretical framework is proposed to analyze comparative sensing performance parameters, including protein binding sensitivity ( S ), the plasmonic dip position ( n 0 sinθ sp ), the full width at half maximum ( FWHM ), the intensity contrast (Δ I ), the average plasmonic dip intensity ( I sp ), and the figure of merit ( FoM ). The theoretical investigation was based on rigorous coupled-wave analysis [ 39 , 40 ] and Monte Carlo simulation [ 41 ].…”

Analysis of Effects of Surface Roughness on Sensing Performance of Surface Plasmon Resonance Detection for Refractive Index Sensing Application

“…The current trend and the state-of-the-art technology for SPR measurement are to measure smaller biological molecule size [ 54 , 55 ] and numbers of molecules [ 56 , 57 ], aiming towards single-molecule detection [ 58 ]. In addition, SPR measurement methods have been proposed to enhance the sensitivity in SPR measurement, including phase detection [ 59 , 60 ], long-range surface plasmon polaritons [ 61 ], and metamaterial surfaces [ 62 , 63 ]. There are, of course, challenges in achieving such measurements, including environmental stability and the quality of the plasmonic sensor surface.…”

Sensing Using Light: A Key Area of Sensors

“…Can terms, concepts, methods, effects, approaches, techniques, technologies, etc., so disparate, as mentioned in [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], all be considered under the same sensing umbrella?…”