Defocus leakage radiation microscopy for single shot surface plasmon measurement

et al. 2022

Sensors

Self Cite

Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.

Section: Can3-can5mentioning

confidence: 99%

Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy

Visitsattapongse

Thadson

et al. 2022

Sensors

Self Cite

“…The FP resonances excited in a thin film-based grating consisting of a thin gold layer and a one-dimensional (1D) rectangular dielectric grating, as shown in Figure 1 a. The FP mode allows convenient sample access from the open space similar to surface plasmon resonance (SPR) detection [ 14 , 15 , 16 ] with a uniform gold layer of 48 nm as shown in Figure 1 b, unlike well-known FP resonators, such as Bragg reflectors [ 8 , 17 , 18 ]. The dielectric grating is a lossless structure; the gold layer provides a loss mechanism [ 15 , 19 ] for the FP mode.…”

Section: Introductionmentioning

confidence: 99%

Fabry–Perot Resonance in 2D Dielectric Grating for Figure of Merit Enhancement in Refractive Index Sensing

Sasivimolkul

Suvarnaphaet

2021

Sensors

Self Cite

We have recently reported in our previous work that one-dimensional dielectric grating can provide an open structure for Fabry–Perot mode excitation. The grating gaps allow the sample refractive index to fill up the grating spaces enabling the sample to perturb the Fabry–Perot mode resonant condition. Thus, the grating structure can be utilized as a refractive index sensor and provides convenient sample access from the open end of the grating with an enhanced figure of merit compared to the other thin-film technologies. Here, we demonstrate that 2D grating structures, such as rectangular pillars and circular pillars, can further enhance refractive index sensing performance. The refractive index theory for rectangular pillars and circular pillars are proposed and validated with rigorous coupled wave theory. An effective refractive index theory is proposed to simplify the 2D grating computation and accurately predict the Fabry–Perot mode positions. The 2D gratings have more grating space leading to a higher resonant condition perturbation and sensitivity. They also provide narrower Fabry–Perot mode reflectance dips leading to a 4.5 times figure of merit enhancement than the Fabry–Perot modes excited in the 1D grating. The performance comparison for thin-film technologies for refractive index sensing is also presented and discussed.

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

Thadson

Visitsattapongse

2021

Sci Rep

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.