1. INTRODUCTION

Surface plasmon resonance microscopy (SPRM) is a near-field sensing and imaging technique that is able to detect tiny changes in object properties within plasmon wave penetration depth with prominently high sensitivity [1]. It provides surface plasmon resonance (SPR) imaging of samples in the near-field region for optical visualization and sensing, which is highly desirable in chemical and biological studies. Due to its high detection sensitivity, high-throughput, nondestructive, noninvasive, wide-field, and label-free imaging advantages, SPRM has excellent capabilities for optical sensing measurements and microscopic imaging and has been widely employed for cellular activities [2–5], antigen–antibody binding processes [6–9], and biomolecular detection [10–13], as well as the characterization of the physical properties of nanoparticles [14–17] and two-dimension materials [18–21]. SPRM is typically constructed by the Kretschmann configuration with a prism or a high numerical aperture (NA) objective. The Kretschmann configuration is the most common SPR excitation configuration, in which a thin metal film is evaporated on top of a glass substrate. Prism-coupled SPRM has a large field of view but low imaging resolution because it cannot be integrated with high magnification objectives due to the limitations of the system structure. While utilizing a high NA and magnification objective, the objective-coupled SPRM effectively improves imaging resolution and avoids imaging aberrations [22].

However, the propagating surface plasmon wave (SPW) is generated at the interface of thin metallic film while SPR occurs. When the SPW interacts with subwavelength objects on the metallic interface, scattering light arises from the interactions and interferes with the reflected light in the imaging optical path since they are coherent, giving tail-shaped interference patterns in the SPR images [23]. This causes an unclear edge of the sample along the direction of SPW propagation and seriously deteriorates the image contrast, which significantly hinders the broad applications of SPRM. The demands for full morphology visualization and higher image contrast have driven research into more advanced approaches to improve SPRM imaging capabilities. The current related approaches can be categorized into four types. The first one is limiting the propagation length of SPW to suppress the “tail” patterns, e.g., by optimizing the light wavelength [24], replacing the metallic film with plasmonically active metamaterials to excite localized SPR [25], upgrading the flattened gold substrate to a grating nanostructure to excite the “hybrid” SPR modes [26], and utilizing high-Q dielectric metasurfaces instead of plasmonic configurations to avoid the generation of SPW [27–29]. Due to the complexity of the optimization and nanofabrication process, these methods are difficult for wide applications. The second approach is acquiring multiple images at different azimuthal incidence angles followed by image fusion algorithms to eliminate the “tail” patterns, such as by switching opposite illumination [30,31], dual-channel simultaneous illumination [32,33], multi-angle illumination [34–36], and rotating the sample [37]. However, this type of method requires several tunable mirrors or galvanometers, making the design of the optical system cumbersome. The third approach is utilizing image processing algorithms to enhance the SPRM imaging capabilities with a single image, such as deconvolution algorithm [8,38] and spectral filtering with a designed mask [39]. Currently, such methods are only applicable to simple samples such as nanospheres and nanowires. The fourth approach employs advanced optoelectronic devices, such as a digital light projector [40] and a hyperspectral microscope [41], to improve the SPRM image quality but it comes at a high system cost.

Here, we propose an incidence angle scanning method in an objective-coupled SPRM to improve the imaging capabilities. By scanning a wide range of incidence angles, we collect a series of intensity images to build a data cube. Then we use the designed algorithm to extract the resonance angle corresponding to the minimum value of the reflectance pixel-by-pixel, and further reconstruct the resonance angle (RA) image in which “tail” patterns and the background noise have been prominently suppressed. As a result, this method is able to reconstruct the complete morphology of samples and provide exceptional imaging contrast. Our method can be easily integrated into the existing SPRM to enhance the imaging capabilities, while possessing the advantages of the high detection sensitivity of SPR. The validity of this method has been demonstrated by imaging photoresist square samples and ${\mathrm{ReS}}_2$ two-dimensional materials in comparison to conventional SPRM.

2. METHOD

A. Method Workflow

The regions above the metallic film interface with different physical parameters, such as varying dielectric constants and thicknesses, require different incidence angles to excite SPR. In other words, at a specific incidence angle, only the regions with the same physical parameters are at the SPR excitation state, leading to the minimum reflectance. Therefore, the regions with different physical parameters will resonate separately by gradually adjusting the incidence angle of the excitation light. During this process, a series of intensity images are recorded. Then, the RA values of all regions are extracted pixel-by-pixel. By relabeling the image with the acquired RA data, the sample morphology can be fully recovered and effectively differentiated from the background, resulting in the superior enhancement of the SPR image contrast. Figure 1 illustrates the workflow of the proposed method.

 

Fig. 1. Workflow of incidence angle scanning SPRM. (a) Performing the incidence angle scanning. (b) Recording a series of intensity images at different incidence angles. (c) Resonance angle extraction with designed data processing procedure. (d) Reconstructing the sample image with full morphology and high contrast.

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B. Experimental Setup

The experimental setup of the objective-coupled incidence angle scanning SPRM is shown in Fig. 2. A monochromatic linearly polarized laser beam from a stabilized diode laser (center wavelength 632.8 nm, 0633-08-11-0030-100, Cobolt, Solna, Sweden) passes through a negative lens to be expanded and then is collimated by a collimation lens. A focusing lens is employed to focus the beam at the back focal plane of the oil immersion microscope objective ($100\times$, 1.49 NA, Nikon, Japan). A plane wave output from the objective then illuminates the Kretschmann configuration in a wide-field manner at incidence angle $\theta$. A half-wave plate is inserted between the focusing lens and objective to generate $\mathrm{p}$-polarization light to excite SPR. The reflected beam from the Kretschmann configuration carrying the sample information is collected by a tube lens and imaged on the CCD (Basler ace acA2040-90um, Ahrensburg, Germany). The fiber output, negative lens, collimation lens, focusing lens, and half-wave plate are mounted on a linear translation stage to adjust the offset between the laser beam and the optical axis of objective (inset of Fig. 2). By controlling the translation stage to linearly move forward step by step we can perform $\theta$ scanning in a wide range. The angular resolution of the experimental system depends on the stepping resolution of the linear translation stage. The unidirectional repeatability of the used linear translation stage is 10 μm and the corresponding angular resolution is 0.43° (see Appendix A for details). The detection limit of the sample refractive index variation is 0.0072 corresponding to the angular resolution (see Appendix B for details). We further calculate the correlation between $\theta$ and the translation stage position $d$ with the formula $d-d_0=f\sin \theta$, where $d_0$ and $f$ represent the original translation stage position and the equivalent focal length of the objective, respectively. SPR intensity images at different incidence angles are captured as the raw data for further image processing.

 

Fig. 2. Experimental setup of incidence angle scanning SPRM. NL: negative lens; CL: collimation lens; FL: focusing lens; HWP: half-wave plate; BS: beam splitter; MO: microscope objective; TL: tube lens; BFP: back focal plane. Inset on the left side shows details of the Kretschmann configuration and incidence angle scanning mechanism.

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C. Data Processing Procedure

The proposed data processing procedure is shown in Fig. 3. First, raw data including SPR intensity images and background images at different incidence angles are captured using the mentioned angle scanning mechanism. Liquid dielectric material such as water is added above the samples to disrupt the resonance conditions to obtain the background images. Then, background subtraction and Fourier space filtering are performed to remove system background noise and interference noise. The processed image data are combined to form a data cube $D(x,y,\theta )$ with size of $s_1\times s_2\times n$, where $s_1$, $s_2$ denote the size of each image and $n$ is the number of incidence angles. $\theta$ needs to satisfy

 

Fig. 3. Data processing procedure of incidence angle scanning SPRM.

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The mathematical principle of the designed algorithm is expressed by

Since the value at each pixel of the image represents the resonance angle, we name the calculated image as the RA image. In fact, the RA image is constructed using the SPR angle information. Since the resonance angles for different positions of the sample are different, the RA image can distinguish different parts of the sample clearly with high contrast. Compared to a conventional SPR image, there are no “tail” patterns in the RA image, the sample edges are sharper, and the morphology of the sample is fully recovered. Due to the pixel-by-pixel calculation manner, each pixel generates an SPR curve. Therefore, effects of coherent illumination, such as intensity variations and interferences, can be significantly reduced, resulting in a remarkably increased imaging contrast.

However, when SPW propagates from the resonant region to the nonresonant one, interference is generated [42] and the dark areas of interference patterns affect the search of minimum $R$, resulting in inaccurate resonance angle calculation and the loss of sample edge information. Therefore, once the RA image is obtained, we further assess the imaging quality based on the sample morphology fidelity and background noise level. Sample morphology fidelity refers to the matching degree between the sample morphology of the RA image and that of the optical microscope image. If the sample edge information is partly lost or the background noise is still obvious, the calibration algorithm is utilized for further correction. We propose to regenerate two RA images [i.e., RA images with small (SRA) and large (LRA) scanning ranges]. For the case of SRA, the incidence angle scanning range does not cover the resonance angle of the background region. In this way, no interference fringes are caused by the cross-region propagation of SPW and thus the SRA image contains complete sample morphology. On the contrary, if the resonance angle of the background region is covered, the generated LRA image has a more uniform and smoother background. A level-set image segmentation algorithm is a digital method for boundary extraction by tracking contours and surface motion that is widely applied for image post-processing. Thus, we apply the level-set segmentation algorithm on the SRA image to produce a mask and an anti-mask representing the sample and background regions, respectively. We use the mask to extract the sample information from the SRA image and the anti-mask to extract the background information from the LRA image. Finally, the extracted information is combined to build a calibrated RA (CRA) image with full sample morphology information and increased image contrast. Note that the large scanning range actually includes the small scanning range, so only the large-range scanning is performed during the experimental data collection.

3. EXPERIMENT RESULTS

A. Photoresist Square Sample

We experimentally verified the feasibility of the incidence angle scanning SPRM by using photoresist square samples with different side lengths as the imaging targets. Photoresist samples on the gold film were fabricated through spinning coating and electron beam lithography. First, we performed incidence angle scanning (the scanning range was set as 40.9°–63.0°) by controlling the linear translation stage to step forward with an interval of 10 μm and recorded a series of SPR intensity images. A total of 71 images were collected and the time consumption was about 14.5 s. It is important to note that the quality of the image reconstruction is directly affected by the number of images collected during the scanning process (see Appendix C for details). Then, the RA image was obtained using the image processing procedure described above. The interference patterns generated by the cross-region propagation of SPW from the photoresist-covered area to the air area seriously hindered the image reconstruction, which caused partial loss of the square edge information in one direction of the initial RA image. Therefore, further corrections for all photoresist square samples with different side lengths were needed to obtain the CRA images by applying the calibration algorithm.

Figures 4(a₁)–4(a₃) show the SPR image in which SPR occurred in the photoresist-covered area, the CRA image, and the bright-field image recorded by a commercial reflected light microscope. It can be seen that the sample edge in the $y$ direction, which was the SPW propagation direction, was completely lost in the SPR image [Fig. 4(a₁)]; at the same time, the edge information was fully recovered in the CRA image of Fig. 4(a₂). The geometric shape of the sample in the CRA image conforms well with the bright-field image [Fig. 4(a₃)], confirming that the CRA image can reflect the sample geometric morphology as reliably as the microscope image. In addition, the contrast of the CRA image is much higher than that of the SPR image. For a further detailed comparison, the cross-section line-cut profiles along the $x$ axis and $y$ axis were extracted from the SPR and CRA images, respectively, as shown in Fig. 4(a₄). All of the profile plots were assigned to square sample areas with an actual size of $15{\mathrm{\mu m}}^{}\times 15{\mathrm{\mu m}}^{}$. The profiles of the CRA image exhibit abrupt changes at the edges in both the $x$ and $y$ directions, while the SPR image profiles show gradients and perturbations in the $x$ direction and barely any edges in the $y$ direction. The differences between the measured sizes based on the CRA image and actual sizes in the $x$ and $y$ directions are 290 nm and 90 nm, respectively, which means that the method allows for precise two-dimensional quantification measurement.

 

Fig. 4. Experiment results of photoresist square sample with side length of (a) 15 μm and (b) 8 μm. Insets are: (a₁), (b₁) SPR; (a₂), (b₂) CRA; and (a₃), (b₃) bright-field image. In (a₁), (b₁), the incidence angle was set as the resonance angle of the background region. (a₄), (b₄) Line-cut profiles along the $x$, $y$ directions of the SPR and CRA images. The dashed lines of (a₁), (a₂), (b₁), and (b₂) mark the locations of the line-cuts. The red arrow in (a₁) indicates the SPW propagation direction and is applicable to (b₁). The color bar of the SPR image indicates normalized intensity and the CRA image indicates the resonance angle. The scale bar in (a₁) is 10 μm and applicable to (a₂), (a₃). The scale bar in (b₁) is 5 μm and applicable to (b₂), (b₃).

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The experiment results of the photoresist square samples with the side length of 8 μm are shown in Fig. 4(b). It can be seen that the sample morphology in the $y$ direction of the SPR image can no longer be effectively distinguished as two neighboring squares. It can, however, be easily identified in the CRA image, which is consistent with the bright-field image. It should be noted that due to the limitation of the image segmentation algorithm, sample boundaries cannot be always accurately extracted. So, there is an edge-bending problem in the CRA image and sometimes the reconstructed square is not the standard square. In addition, some speckle noises are still present in the CRA image, which may be caused by the combination effect of the “tail” patterns and inherent noises from the coherent light illumination. A higher accuracy of image segmentation is foreseen to make use of more sophisticated neural network-based image segmentation methods and machine learning.

To quantitatively compare the image contrast, the contrast-to-noise ratio (CNR) is adopted as the figure-of-merit and defined as [43]

 

Fig. 5. The CNRs of the SPR images and the CRA images for the photoresist square samples with different side lengths. The insets are the CRA images and SPR images of the photoresist square samples with different side lengths while the scale bar is 5 μm.

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B. ReS₂ Two-Dimensional Materials

To further verify the feasibility of the proposed method, we conducted an experimental study on ${\mathrm{ReS}}_2$ two-dimensional (2D) materials. The ${\mathrm{ReS}}_2$ materials were transferred onto the Au substrate with the mechanical stripping and dry transfer process. The data collection process was the same as the photoresist square sample. The results obtained by using incidence angle scanning SPRM are presented in Fig. 6. Since the thickness of the ${\mathrm{ReS}}_2$ 2D materials is only a few dozen nanometers, as proven in the atomic force microscope (AFM) measurement results in Fig. 6(d), the disruption induced by the cross-section propagation of SPW is limited. This enables direct reconstruction of the high-contrast RA image with complete morphology of the materials without applying the calibration algorithm. The SPR image shown in Fig. 6(b) is unable to distinguish the edge of the ${\mathrm{ReS}}_2$ material along the propagation direction of SPW, while the RA image in Fig. 6(c) has clearly distinguishable edges and conforms well with the bright-field image [Fig. 6(a)]. The edge sharpness and quantification measurement of the materials can be further investigated from the cross-section line-cuts of the images. From the profile distributions in Fig. 6(e), no edge can be found in the SPR image, but there is a clear jump at the edges in the RA image. The lengths of the long and short sides were measured to be 3.963 μm and 2.426 μm, and the difference between the measurements and AFM results is 53 nm and 120 nm, respectively. We further quantitatively evaluated the image contrast with the CNR metric for the ${\mathrm{ReS}}_2$ 2D materials, as depicted in Fig. 6(f). The results show that the RA images have an average twofold higher CNR compared to the SPR images.

 

Fig. 6. Experiment results of ${\mathrm{ReS}}_2$ 2D materials. (a) Bright-field image. (b) SPR image. Color bar indicates gray values. The incidence angle was set as the resonance angle of the background region. (c) RA image. Color bar indicates the resonance angle. The inset of (a) is the corresponding AFM image. Scale bar in (a) is 5 μm and applicable to (b), (c). The red arrow in (b) indicates the SPW propagation direction. (d) The AFM profiles of the thickness along the white dashed cross-section lines in the inset of (a). (e) Cross-section line 1 (2)-cut profiles of the SPR and RA images. Dashed lines of (b), (c) mark the locations of the line-cuts. (f) CNRs of SPR images and RA images for ${\mathrm{ReS}}_2$ 2D materials with different shapes. Insets are the RA images and SPR images for the corresponding samples. Scale bar is 10 μm.

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The experiment results above demonstrate that the RA image obtained from incidence angle scanning SPRM with the designed data processing procedure is superior to the grayscale SPR image in terms of the image quality, especially for sharpening the edges of the samples. It provides the complete morphology of the samples with exceptional image contrast. Besides, we can perform precise 2D quantification measurements using the RA image, which is incapable in conventional SPRM.

It is worth mentioning that not all samples are applicable to this method. Samples that are compatible with this method should feature the following characteristics. First, SPR can be excited in the sample area within the scanning range of incidence angle. Second, the sample should possess a certain surface area when adhered to the gold film. Besides, all of these factors have an impact on CNR calculation: the surface flatness of the coverslip and the gold film fabricated with the E-beam evaporator, the etching accuracy of the electron beam lithography for photoresist sample fabrication, and the thickness and shape of the two-dimensional material and its adhesion degree to the gold film. Therefore, the CNRs of samples with different shapes or diverse sizes show discrepancies.

4. CONCLUSION

In conclusion, we have proposed and demonstrated a flexible incidence angle scanning SPRM for enhanced SPR imaging. With photoresist square samples and ${\mathrm{ReS}}_2$ samples, we have experimentally demonstrated that the RA image obtained by the proposed data processing procedure can fully reconstruct the sample morphology, prominently eliminate the influence of SPW propagation, and significantly improve the image contrast. We have evaluated the improvement of the image contrast quantitatively using a defined metric contrast-to-noise ratio and the results indicate that a maximum fivefold increase can be achieved. Besides, we can perform precise 2D measurements with an error margin of hundred nanometers, which is incapable in conventional SPRM. Compared to conventional SPRM, our method operates in an angular scanning mode and has flexible features. First, it can provide two types of images, including a grayscale SPR image and the RA image. Second, it enables full morphology imaging with high contrast based on single-pixel angular scanning. Third, it can be used to measure the 2D profiles of dielectric samples using the RA image. Fourth, because the setup is almost the same as conventional SPRM, the method can be easily migrated to upgrade existing SPRM with low cost and stable performance. With the remarkable features and excellent performance mentioned above, our method will promote the application of SPRM in label-free detection, imaging, and quantification of chemical and biological measurands.

APPENDIX A: MATHEMATICAL CALCULATION OF ANGULAR RESOLUTION $\mathrm{\Delta }\theta$

The correlation between the incidence angle $\theta$ and the translation stage position $d$ is given as

(A1)$$d-d_0=f\sin \theta ,$$

(A2)$$d+\mathrm{\Delta }d-d_0=f\sin (\theta +\mathrm{\Delta }\theta ),$$

where $d_0$ represents the original translation stage position, $f$ represents the equivalent focal length of the objective (2 mm in our experimental system), $\mathrm{\Delta }d$ is the stepping resolution of the linear translation stage, and $\mathrm{\Delta }\theta$ is the angular resolution. Solving the equation above, we can get

(A3)$$\mathrm{\Delta }d=f[\sin (\theta +\mathrm{\Delta }\theta )-\sin \theta ].$$

In our experimental system, the unidirectional repeatability of the linear translation stage (i.e., $\mathrm{\Delta }d$) is 10 μm. The corresponding relationship between $\mathrm{\Delta }\theta$ and $\theta$ is plotted in Fig. 7 based on Eq. (A3). As we can see, $\mathrm{\Delta }\theta$ is nonlinearly related to $\theta$ and the growth rate of $\mathrm{\Delta }\theta$ accelerates with the increase of $\theta$. When collecting the experimental data, the scanning range of $\theta$ is set as 40.9°–63.0°, and the corresponding angular resolution $\mathrm{\Delta }\theta$ changes between 0.26° and 0.43°. Therefore, the angular resolution of our experimental system technically is 0.43°.

 

Fig. 7. Relationship between the incidence angle $\theta$ and angular resolution $\mathrm{\Delta }\theta$.

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APPENDIX B: DETECTION LIMIT OF PHYSICAL PARAMETER VARIATION

It has been demonstrated that the achievable minimum angle change by the system is 0.43°. When the resonance angle shift induced by the physical parameter variation of the sample is equal to the angular resolution, the amount of the physical parameter variation is the detection limit of the experimental system. The refractive index change information is highly desirable in chemical and biological fields. Taking the refractive index change of the sample as an example, we calculated the detection limit of the refractive index variation based on the three-layer SPR excitation model, as shown in Fig. 8. When the resonance angle differs by 0.43°, the corresponding refractive index variation is 0.0072.

 

Fig. 8. Detection limit of the refractive index variation corresponding to the angular resolution of the experimental system.

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APPENDIX C: ANALYSIS BETWEEN ANGULAR RESOLUTION AND IMAGE CONTRAST ENHANCEMENT

The angular resolution depends on the stepping interval of the linear translation stage. We applied the stepping intervals of 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, and 60 μm, respectively, to collect the experimental data and reconstruct the corresponding RA images, as shown in Figs. 9(a1)–9(a6). Intuitively, the image contrast is gradually decreasing as the stepping interval increases. We further calculated their CNRs, as shown in Fig. 10. The CNR gradually declines as the stepping interval increases. In other words, the image contrast enhancement is dropping as the angular resolution decreases. Due to the presence of prominent speckle noise in the background, the CNR of the RA image reconstructed with the stepping interval of 40 μm drastically reduced. We further exhibit the corresponding scanning points in the reflectance curves of the sample and background, as shown in Figs. 9(b1)–9(b6). It can be seen that increasing the stepping interval introduces bias in resonance angle extraction, which would lead to a reduction in the image contrast. Therefore, in this paper we set the stepping interval as 10 μm, and a total of 71 images were collected to get the best CNR.

 

Fig. 9. The experiment results of the photoresist square sample with a side length of 15 μm. (a₁)–(a₆) Resonance angle image reconstructed by using the data collected with 10, 20, 30, 40, 50, and 60 μm stepping intervals of the linear translation stage. The color bar indicates the resonance angle and is applicable to (a₁)–(a₆). The scale bar in (a₁) is 5 μm and applicable to (a₂)–(a₆). (b₁)–(b₆) The scanning points in the reflectance curves of the sample and background. The number of scanning points for (b₁)–(b₆) is 71, 36, 24, 18, 15, and 12, respectively.

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Fig. 10. The CNRs of the resonance angle images for the 15 μm photoresist square sample with different stepping intervals of the linear translation stage.

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Funding

National Natural Science Foundation of China (61927810, 62275219).

Disclosures

The authors declare no conflicts of interest.

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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