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Surface-plasmonometry: high-resolution and model-free plasmonic measurements of refractive index and its biosensing application Jisoo Kyoung, Hee Eun Kang, and Sungwoo Hwang ACS Photonics, Just Accepted Manuscript • Publication Date (Web): 24 Mar 2017 Downloaded from http://pubs.acs.org on March 25, 2017

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Surface-plasmonometry: high-resolution and modelfree plasmonic measurements of refractive index and its biosensing application Jisoo Kyoung1,*, Hee Eun Kang2, and Sung Woo Hwang1 1 2

Samsung Advanced Institute of Technology, Samsung Electronics Co., Suwon-si 443-803, Korea

College of Pharmacy and Integrated Research Institute of Pharmaceutical Sciences, The Catholic University of Korea, Bucheon, South Korea *

[email protected]

Abstract: The refractive index is crucial in determining the detailed nature of the propagation of the electromagnetic waves in a medium. There is a growing demand for high-resolution measurement of refractive index because newly synthesized materials are usually small and optical devices and the elements of metamateirals are being miniaturized. In addition, the inhomogeneous broadening effects of the organic materials cannot be described by the mathematical dielectric models, which are essential to extract refractive index from conventional spectroscopic measurements. Here, we present a novel method called surface-plasmonometry that extracts the complex refractive index of a material using surface plasmons without any dielectric models. By changing the length of the nano-slot antennas, various plasmon modes are excited and coupled to the target material, which becomes a unique characteristic of the refractive index. Since the optical parameters can be extracted at each wavelength through a machine learning algorithm, mathematical dielectric models are not necessary. Furthermore, with the table-top Fourier transform infrared (FT-IR) source and detector, we achieve 10,000 times enhancement of lateral resolution compared to the conventional ellipsometry owing to the extreme locality of the surface plasmon and the normal incidence geometry. We also successfully demonstrate the label-free detection of protein bilayer by the surface-plasmonometry, which opens an exciting new biosensing platform. Key words: surface plasmon, slot antenna, mid-IR, refractive index, label-free detection, protein, machine learning

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Refractive index of a medium is one of the most fundamental optical parameters, which is used to explain various light-matter interactions. The real and imaginary parts determine the speed of light and absorption coefficient in the medium, respectively. Snell’s law and Fresnel coefficients are functions of refractive index. This means that once the refractive index is known, light transmission and reflection characteristics are predictable at an arbitrary angle of incidence. In addition, the dielectric constants are given by the square of the refractive index. Therefore, when a new material is synthesized or discovered, many researchers try to measure the index of refraction in a wide spectral range in order to investigate its optical applications. In the visible wavelength regime, spectroscopic ellipsometry has been widely used for measuring the refractive index of well-defined thin films. The linearly polarized beam obliquely incidents on the target material, and undergoes a change in the polarization state upon specular reflection. The ellipsometer measures the amplitude ratio and phase difference between the p- and s-polarizations of the reflected light. The term ellipsometry originated from the fact that the polarization state mostly becomes elliptical through reflection. To avoid the technical difficulties in measuring the amplitude and phase, spectroscopic measurements are generally required.1 Once the amplitude and phase spectra are obtained, the refractive index can be extracted after fitting them with the proper dielectric model, such as the Cauchy, Drude-Lorentz, Tauc-Lorentz, or Brendel models.2 In fact, the construction of an appropriate optical model is essential for data analysis because the direct extraction of the refractive index from the spectrum is in general not possible; the optical constants and the transmission/reflection coefficients do not fulfill the a one-to-one correspondence owing to the transcendental equations between them. Unfortunately, however, there are certain problems in applying mathematical models. First of all, these models are contrived to satisfy Kramers-Kronig relation, which is not the case for Gaussian line shape. Namely, the Gaussian function cannot be described by the mathematical dielectric models. Especially for organic materials which are important for organic light emitting diodes, organic photovoltaic cells, and daily life, Gaussian function is necessary to explain the inhomogeneous broadening effects in the absorption lines3–6. Therefore, mathematical models, for example Lorentzian oscillator model, are not appropriate to precisely describe the absorption behavior of organic semiconductors or protein. Second drawback is the ambiguity of the model selection. The number and position of oscillators in semiconductors, dielectrics, and biomolecules need to be carefully investigated through refractive index measurement. However, refractive index of a newly synthesized material is not yet known. If we try to measure its refractive index, selecting the dielectric model is uncertain and an unsuitable dielectric model yields considerable errors. Even though a proper model is selected, the number of oscillators should be specified a priori. Consequently, there is a need for a method that allows model-free measurement of the refractive index in order to achieve successful characterization of unknown materials. Another disadvantage of conventional ellipsometry is the low lateral resolution. In other words, the minimum size of the measurable sample is quite large compared to the wavelength. For example, the minimum sample size required is 10 mm by 10 mm for a table top mid-IR ellipsometer (2~16-µm-wavelength). We introduce a figure-of-merit (FoM) in the refractive index measurement as the inverse of the lateral resolution. Then, the FoM for a mid-IR ellipsometer is about 0.01/mm2 = 10-8/µm2. Recently, the optical components are 2 / 13


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being miniaturized and metasurfaces and metamaterials are being constructed by the subwavelength optical atoms to achieve the unexpected optical properties, such as unnatural reflection angle7, flat lens8,9, and chiroptical spectrometer10. The performances of these devices depend on the uniformity of the dielectric constants and the thickness of the used materials. In addition, newly synthesized materials are usually small, few square micrometers or less. Therefore, there is an increasing need for lateral characterization of samples or devices with fine lateral resolution. In fact, the need for large samples is a fundamental constraint in ellipsometry because of the glancing angle of the incident beam. The incident angle is selected to be close to the Brewster’s angle in order to ensure high sensitivity, where the reflection coefficients of the p- and s-polarization beams are maximally different. Typically, the incidence angle is 70o~80o depending on the target materials. After all, there is a trade-off between the sensitivity and the lateral resolution (minimum measurable size). Tight focusing could not be an ultimate solution because the angle of incidence diverges, which produces large errors in the measurement of optical constants11–13. Recently, it was reported that the use of a brilliant light source such as a synchrotron can reduce the lateral resolution to up to a few hundred micrometers (FoM~10-5/µm2)14. Also, the near-field microscopy instruments have been used to measure the complex refractive index15–17. They allowed two orders of magnitude improvement in spatial resolution without using the dielectric models at midinfrared frequencies. However, achieving fine lateral resolution with table-top sources and detectors is important for measurements without sample damage and for the miniaturization of sensing systems. Surface plasmons, which refer to collective charge oscillations at the metal surface, have become one of the most valuable tools for ultra-sensitive chemical and biological sensor applications because of their extreme sensitivity to the surrounding dielectric environment18,19. In the presence of the specific molecules, the refractive index near the metal nano-structures changes, which induces a drastic shift and deformation of the surface plasmon resonance (SPR)

20,21

. Indeed, the SPR contains the information about the optical constants of the

nearby materials. There have been several works reporting the measurements of the optical parameters using surface plasmon resonance. However, they still use either mathematical dielectric models22 or angle-resolved spectroscopy with variation of the refractive index of the target solutions.23,24 Here, we report a novel method to extract the complex refractive index of a material using surface plasmons via a machine learning algorithm, and demonstrate its potential for overcoming the limitations of the current methodologies in the mid-IR regime. Since we only use the features of the surface plasmon itself without any mathematical models or angular spectroscopy, we call this method surface-plasmonometry named after ellipsometry. Furthermore, owing to the extreme locality of the surface plasmon and the normal incidence geometry, we achieved 10,000 times enhancement of the FoM (~10-4/µm2) with the conventional Fourier transform infrared (FT-IR) source and detector. In order to validate our new method, we first measure the complex refractive index of well-known dielectric film, silicon dioxide (SiO2), and then compare them with the literature values. The 300-nm-thick SiO2 film was grown by thermal chemical vapor deposition (CVD). Its refractive index dynamically changes especially in mid-IR range, owing to the strong phonon absorption11,25,26. To generate various surface plasmon resonances, we prepared 31 types of nano-plasmonic structures, each of which consisted of the periodic array of the nano-slot antennas (figure 1(a)). The x axis period (3.5 µm) was same for all samples, while the length of the nano-slot antennas was varied from l = 0.5 µm to 15.5 µm and the y axis period was designed to be 0.5 µm 3 / 13


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longer than the length. The width of each nano-slot antenna was 500 nm. The strong interaction between the plasmon and the nearby materials appears in the form of the unique transmission curve, which can be measured by conventional FT-IR equipment in normal incidence geometry (figure 1(a)). The incident beam was unpolarized. The transmission as a function of the nano-slot length is displayed in figure 1(b) (red triangle) at a wavenumber of 760 cm-1. The transmission rapidly grows until 0.22 at l = 4.5 µm, and then gradually decreases. To extract the optical parameters from the characteristic transmission curve, we constructed a fourdimensional (4D) library (figure 1(c)) through analytic calculation27, consisting of the transmission, complex refractive index (nr + ini, nr, ni: real), and nano-slot length. It is necessary to assume that the target material is a uniform medium to calculate the transmission analytically. If the materials are inhomogeneous or anisotropic, the finite difference time domain (FDTD) simulation could be employed instead. Each 3D contour plot in figure 1(c) represents the transmission versus complex refractive index at a specific length (written at the top of the graph). Because of the limited space, only three contour plots for l = 0.5, 8.0, and 15.5 µm are shown. The next step is to find the geodesic line in the 4D library (the white arrow in figure 1(c)), along which the transmission set is the closest to the measurement data in figure 1(b). The k-nearest neighbors (k-NN) algorithm, one of the most popular machine learning algorithms, was employed to perform this task. The k-NN algorithm is a nonparametric method widely used for solving the classification or regression problem28. In the k-NN regression, the input consists of the k closest training examples of each query point, where k is a positive integer value specified by the user, and the output is the property value for the object. The basic k-NN algorithm for regression uses uniform weights, so that each point in the local neighborhood contributes uniformly to the regression of a query point. Another common weighting scheme is the distance assigning the weights proportional to the inverse of the distance from the query point. In our case, the input of the training set for the learning stage was the calculated length dependent transmission and the output was the corresponding real and imaginary parts of refractive index. After training our machine with the 4D library, the complex refractive index for our measurement data (figure 1(b), red triangle) could be predicted. At a wavenumber of 760 cm-1, the machine’s answer was 1.74 + 0.24i, when the number of the nearest neighbors was five (k = 5) with the uniform weights option. We discovered that there were no significant differences according to the weights option (see the supplementary figure S1). The kdependence is discussed in supplementary figure S2. It should be emphasized that the transmission curve with 1.74 + 0.24i was not contained in our 4D library, because our calculation was performed every 0.1 step in n and k. In other words, the significant digits extended to at least the second decimal place, which we had not considered. This is the major advantage of the machine learning algorithm in that it can yield results beyond the given data apart from the conventional least square method. In fact, least square method does not give satisfactory results. Our k-NN machine learning algorithm becomes least square algorithm when k is unity. The result of least square algorithm is shown in Fig. S2 (k=1 case). As can be seen, the least square method gives distorted results especially between 1000 cm-1 and 1200 cm-1. In order to obtain the comparable results with conventional least square method, at least 100 times more data points are required in a single 4D library. This means that our machine learning algorithm is quite efficient and more accurate than the conventional least square algorithm. The calculated transmission with the predicted refractive index, 1.74 + 0.24i, is exhibited in figure 1(b) (cyan circles). As clearly seen, the measurement and the machine learning result agree very well 4 / 13


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indicating that our novel method, surface-plasmonometry, can successfully extract the complex refractive index. The deviation of the experimental data from the best fitting curve in the long antenna regime is mainly originated from the imperfection of the samples. The slot antenna patterns are made by standard photolithography and lift-off technique. If the pattern size is small, the sample surfaces are very clean after liftoff process. However, as the pattern size becomes larger, some pieces of debris remain near the slot antennas due to their heavy weight. Such defects eventually cause the measured transmission to deviate from the calculated value. Likewise, we selected forty more wavenumbers in the range of 700 cm-1 to 1900 cm-1 at almost equal intervals and applied the surface-plasmonometry to each point. Finally, the whole refractive index dispersion curve of the 300-nm-thick SiO2 film was obtained as shown in figure 2(a) (cyan circles: real part; red triangles: imaginary part). We can observe the anomalous dispersion (1020~1250 cm-1) associated with a decrease in n with wavenumber, and the strong resonant absorption originated from the transverse optical phonon (1088 cm-1) of the SiO2 film. This characteristic dispersion curve is the unique characteristic of the SiO2 material. Note that we use neither the mathematical dielectric model nor angular spectroscopy to obtain the total refractive index curves in figure 2(a). Figure 2(b) shows the extracted refractive index using the commercial ellipsometer equipment with the same SiO2 film and figure 2(c) is taken from reference17, which reports results for the bulk SiO2 material. Our result is very similar with that in the literature (figure 2(a) and (c)). The peak/dip positions and overall line shapes match well each other. However, there are several differences. For example, nr is slightly lower around 1200 cm-1 in figure 2(a) than that in figure 2(c) probably because our sample is different from bulk one. Also, nr is steadily rising and ni is apart from zero in figure 2(a), while nr is asymptotic and ni is nearly zero in higher wavenumber regime in figure 2(c). This difference originates from that fact that the mathematical model is used for bulk, which has asymptotic behavior of nr and near zero values of ni. Meanwhile, ellipsometry (figure 2(b)) is unsuccessful at describing the small local variations in the dispersion curve (for example, see the areas indicated by the cyan and red arrows in figure 2). We argue that this is the current limitation of the mid-IR 29,30

ellipsometry because such phenomenon is also observed in other literatures

. In general, the ellipsometry

signal becomes worse as the film thickness becomes thinner. In our case, the film thickness is only 2.4 % of the wavelength. This may be the cause of missing peaks near 800-cm-1-wavenumber. Unlike the failure of the ellipsometry, the surface-plasmonometry successfully resolves the small peaks at 800-cm-1-wavenumber and recovers the literature features of bulk samples. This is because the surface-plasmonometry uses near field which is highly confined at the nanometer scale. Therefore, we maintain that surface-plasmonometry is more sensitive than ellipsometry for extracting the optical parameters of thin films. In addition, the lateral sample size of SiO2 thin film for ellipsometry was about 20 mm by 20 mm, while the entire area of slot antenna patterns for surface-pasmonometry was about 200 µm by 200 µm. Because the required sample size is 10,000 times smaller, we are able to state that our method has increased the resolution by a factor of 10,000. If a molecule is close to a resonant plasmonic structure it is common to observe changes in the molecular absorption spectrum. As a result, the refractive index of that material shows non-bulk properties. Nevertheless, as we discussed above, the measured refractive index by surface-plasmonometry is almost the same as the bulk values. We think that there are two reasons why our experiments do not show shift of refractive index by the plasmonic structures. First, the resonance positions of most slot antennas are not matched to those 5 / 13


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of molecules. Therefore, the substrate-antenna coupling is quite small to shift the refractive index of the material. Second, the region which experiences shift of refractive index is negligibly small compared to the entire substrate even though there exists a coupling between the antenna and the substrate. Meanwhile, since we used relatively wide gap (500 nm), the field enhancement is insignificant according to our recent work.26 Thus, the Kerr effect does not occur in our experiment. The surface-plasmonometry was also applied to various oxide films such as silicon nitride (SiN), titanium dioxide (TiO2), and aluminum oxide (Al2O3). The SiN, TiO2, and Al2O3 thin films were deposited by the ICPCVD (inductively coupled plasma chemical vapor deposition), sputtering, and ALD (atomic layer deposition) machines, respectively. The thicknesses of TiO2 and SiN were 300 nm and that of Al2O3 was 42 nm. The finally extracted complex refractive indices are exhibited in figure 3 for (a) SiN, (b) TiO2, and (c) Al2O3 together with the literature values2 of (d) SiN, (e) TiO2, and (f) Al2O3, respectively. The peak/dip positions and overall shapes of our results are in good agreement with those of the literature measured by mid-IR ellipsometry. It should be pointed out that the 4D library, once constructed, is not dependent on the kind of the target material; it can be used repetitively for any materials if the thickness of the film and the plasmonic structures for the transmission curve are matched with the conditions of the 4D library. Note also that the thickness of the film should be known in advance to successfully construct 4D library. Otherwise, we may construct 5D library instead of 4D library to include thickness dependence. However, we think that this is not an efficient way. There are two reasons. First, because the thickness of thin films varies from a few nanometers to hundreds of micrometers, constructing 5D library is quite cumbersome. Second, the thickness of the thin film can be measured easily by using various methods such as reflectometry, AFM (atom force microscopy), and visible ellipsometry. In our case, we used reflectometry to confirm the thicknesses of thin films. We employed the surface-plasmonometry to identify the protein molecules in label-free fashion without a mathematical model. Preparation of protein bilayer (protein A/G and IgG) on slot antenna arrays on SiO2/Si substrate was performed according to methods described in previous studies22,31. Protein A/G at a concentration of 1 mg/ml in 10 mM phosphate-buffered saline (PBS) was first spotted on the slot antenna array and incubated for 1 h at 4˚C to allow physical adsorption. Following removal of unbound protein by washing with PBS, goat anti-mouse IgG at a concentration of 1 mg/ml in PBS was secondly spotted on the array and incubated for 1 h at 4˚C. The assay of protein bilayer on slot antenna array was carried out after the second washing with PBS and final washing with distilled water (figure 4(a)). The main vibration modes of proteins are located at the amide I (1620 - 1680 cm1, lime-green colored region in figure 4(b)) and amide II (1510 - 1580 cm1, hot-pink colored region in figure 4(b)) bands. Amide I absorption originates from the C=O stretching vibration, while Amide II band associated with the N-H bending and C-N stretching vibrations in the amide functional group (see the inset in figure 4(b)). For simplicity, we regarded the protein layer as a 8-nm-thick thin film uniformly covering the top of the slot antennas8. Figure 4(b) shows the extracted refractive index of the bilayer protein (cyan circles: real part, red triangles: imaginary part), where the solid lines are guides to the eye. Two resonant peaks were observed in the imaginary part of refractive index, which corresponded to the two absorption bands of the protein. As clearly seen, the measured peak positions and the spectral ranges of the amide band overlap well, which means that the label-free detection of protein by the surface-plasmonometry is successful without 6 / 13


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mathematical models. Compared to the results of using a mathematical model22, our data appears to violate the Kremer Kronig relation. In fact, the mathematical dielectric models have been designed to satisfy the KramersKronig relation. Accordingly, the refractive index measured using a mathematical model automatically follows the relationship. On the other hand, at the current stage, it is hard to check the Kramers-Kronig relation of protein refractive index measured by surface-plasmonometry because the numbers of data are somewhat less. Due to the limitations of our computing power, the measured wavenumber interval was kept about 20 cm-1. Such interval was enough for label-free detection, whereas not enough for verifying the Kramers-Kronig because the refraction index of protein changes dramatically in a very narrow region. For instance, if the measured peak/dip values are slightly changed, the peak/dip positions would be largely shifted, which distorts the refractive index curves. Therefore, improving the resolution as well as accuracy is required to discuss about the Kramers-Kronig relation. Although ellipsometry is widely used for measuring the refractive index in the visible and NIR (near infrared) range, applying it in the longer wavelength regime (mid-IR, THz, or microwaves) is a challenging task because of relatively low-grade optical elements, such as polarizers, lens, or compensators. Since ellipsometry requires the use of polarized beams, the linear polarizer plays the most important role in the measurements. However, the extinction ratio of commercial wire-grid polarizer (~30 dB) in mid-IR range or the CNT polarizer (~40 dB )32 in THz range is still low compared to that in the visible range (~60 dB). We would like to point out that none of polarizers, analyzers, and compensators were necessary for the surface-plasmonometry apart from the conventional ellipsometry. Instead, we just used a simple transmission set up with normal incidence geometry. This unique characteristic of the surface-plasmonometry enables high signal-to-noise ratios during the measurements. The mid-IR and THz (terahertz) spectral range is technologically important especially for chemical- and bio-sensing applications because numerous molecular vibration modes are located in these regimes. Through absorption spectroscopy, the various chemical- and bio-materials such as ethylene, ammonia, glucose, protein, and DNZ can be identified.33,34 However, the absorption signals are generally weak owing to the tiny sizes of the molecules (~10 nm) compared to the wavelengths (2–300 µm). To increase the absorption cross section, resonant nanostructures have been employed to exploit the strong near field enhancement at the resonance.31,35,36 While achieving high sensitivity, this approach is difficult to be used commonly because the absorption spectra vary significantly depending on the shape of the metal nanostructures even when detecting the same materials. This is because the narrow bandwidth and the non-uniform field enhancement originated from the resonant metallic structures.31,37 Instead, measurement of the refractive index of the target material can be an alternative way for label-free detection.22 In fact, the resonance positions of our nano-slot antennas change substantially according to the antenna length, and even no appreciable resonance is observed with the antennas shorter than 1.5 µm (not shown). Nevertheless, the optical parameters are extracted quite well in the broad spectral range, as discussed in figure 2 (a). In conclusion, we successfully measured the complex refractive index of several materials in the technologically important mid-IR range by the surface-plasmonometry. The super locality and extreme sensitivity are two key characteristics of surface plasmon resonance enabling the surface-plasmonometry. In short, the surface-plasmonometry is composed of three steps: First, measure the characteristic transmission 7 / 13


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curves determined by the strong coupling between the various surface plasmon modes and the surrounding medium. Second, construct 4D library consisted of the transmission as functions of the refractive indices and parameters of the plasmonic structures through calculations. Third, extract the complex refractive index from the 4D library using the machine learning algorithm, which best describes the measured characteristic transmission curve. Unlike previous methods, our method does not require dielectric models or angular spectroscopic measurements. Therefore, the surface-plasmonometry could be used to validate the mathematical dielectric modes as well as the Kramers–Kronig relations after further enhancing the accuracy of the experiments. Furthermore, owing to the subwavelength-scale light confinement by the surface plasmons, the surface-plasmonometry can use samples that are at least four orders of magnitude smaller than the samples used in the conventional ellipsometry. These results demonstrate the ability of the surface-plasmonometry for labelfree detection of completely unknown bio-molecules or for the optical characterization of a tiny 2D material.

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Fig. 1. Surface-plasmonometry. (a) Conceptual view of the surface-plasmonometry. The gold slot antennas of various lengths are placed on the target material (SiO2). The width of each slot is 500 nm and the length changes from 0.5 µm to 15.5 µm with interval of 0.5 µm. The unpolarized incident beam comes in vertically and the transmittances are measured by changing the length of the slit antennas. (b) The measured transmission curve as a function of slot antenna length at the 760-cm-1-wavenumber (red triangles) and the best matched transmission curve (cyan circles) found by the machine learning algorithm (k nearest neighbor) from the 4D library (see figure 1(c)). The extracted refractive index is 1.74 + 0.24i at 760-cm-1-wavenumber. (c) 4D library (transmission maps as a function of refractive index (nr + ini) and slot lengths) constructed through the analytic calculation at 760-cm-1-wavenumber. Each transmission map is the calculated transmission with various n and k values at a certain slot antenna length (l). Our machine learns the whole 4D library and gives the best refractive index describing the measured data. The dashed line represents the transmission curve along the refractive index 1.74 + 0.24i in the 4D library, which is matched best with the measured transmission (figure 1(b), red triangles).

Fig. 2. Refractive index of a SiO2 thin film measured by the surface-plasmonometry. (a) The refractive index dispersion curve of the SiO2 thin film (300-nm-thick) found by the surface-plasmonometry. The cyan circles and red triangles represent the real (nr) and imaginary (ni) part of the refractive index, respectively. The lines are for the guide to the eye. (b) The refractive index of the same SiO2 thin film measured by the conventional ellipsometer. (c) Bulk SiO2 refractive index data from the reference book (ref. 9). Fig. 3. Refractive index of various thin films measured by the surface-plasmonometry. (a) SiN (300-nmthick). (b) TiO2 (300-nm-thick). (c) Al2O3 (42-nm-thick). Literature values of refractive index from ref. 2 for (d) SiN, (e) TiO2, and (f) Al2O3. Fig. 4. Label-free detection of protein bilayer. (a) Protein A/G and IgG form well-defined protein bilayer. (b) Measured real and imaginary parts of the refractive index of the protein bilayer. (inset: schematic diagram of protein vibrational fingerprints, Amide I and Amide II bands)

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Supporting Information Additional discussion about the k-NN algorithm (PDF)

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ACS Photonics

Figure 1. 1 2 3 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(a)

(c) x y

Einc 760 cm-1

Au Si

SiO2

Eout

(b)

Fig. 1. Surface-plasmonometry. (a) Conceptual view of the surface-plasmonometry. The gold slot antennas of various lengths are placed on the target material (SiO2). The width of each slot is 500 nm and the length changes from 0.5 μm to 15.5 μm with interval of 0.5 μm. The unpolarized incident beam comes in vertically and the transmittances are measured by changing the length of the slit antennas. (b) The measured transmission curve as a function of slot antenna length at the 760-cm-1-wavenumber (red triangles) and the best matched transmission curve (cyan circles) found by the machine learning algorithm (k nearest neighbor) from the 4D library (see figure 1(c)). The extracted refractive index is 1.74 + 0.24i at 760-cm-1-wavenumber. (c) 4D library (transmission maps as a function of refractive index (nr + ini) and slot lengths) constructed through the analytic calculation at 760-cm-1-wavenumber. Each transmission map is the calculated transmission with various n and k values at a certain slot antenna length (l). Our machine learns the whole 4D library and gives the best refractive index describing the measured data. The dashed line represents the transmission curve along the refractive index 1.74 + 0.24i in the 4D library, which is matched best with the measured transmission (figure 1(b), red triangles).


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ACS Photonics

Figure 2. (a)

(b)

(c)

Fig. 2. Refractive index of a SiO2 thin film measured by the surface-plasmonometry. (a) The refractive index dispersion curve of the SiO2 thin film (300-nm-thick) found by the surface-plasmonometry. The cyan circles and red triangles represent the real (nr) and imaginary (ni) part of the refractive index, respectively. The lines are for the guide to the eye. (b) The refractive index of the same SiO2 thin film measured by the conventional ellipsometer. (c) Bulk SiO2 refractive index data from the reference book (ref. 9).


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Figure 3. (a)

(b)

(c)

(d)

(e)

(f)

Fig. 3. Refractive index of various thin films measured by the surface-plasmonometry. (a) SiN (300-nmthick). (b) TiO2 (300-nm-thick). (c) Al2O3 (42-nm-thick). Literature values of refractive index from ref. 2 for (d) SiN, (e) TiO2, and (f) Al2O3.


Page 17 of 18 Figure 4. 1 (a) 2 3 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Photonics

(b)

Amide-I C H Amide-II

N C

O H H

Fig. 4. Label-free detection of protein bilayer. (a) Protein A/G and IgG form well-defined protein bilayer. (b) Measured real and imaginary parts of the refractive index of the protein bilayer. (inset: schematic diagram of protein vibrational fingerprints, Amide I and Amide II bands)


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For Table of Contents Use Only Surface-plasmonometry: high-resolution and modelfree plasmonic measurements of refractive index and its biosensing application Jisoo Kyoung1,*, Hee Eun Kang2, and Sung Woo Hwang1 1 2

Samsung Advanced Institute of Technology, Samsung Electronics Co., Suwon-si 443-803, Korea

College of Pharmacy and Integrated Research Institute of Pharmaceutical Sciences, The Catholic University of Korea, Bucheon, South Korea *

x y

[email protected]

Einc Au Si

SiO2

Eout


Surface Plasmonometry: High-Resolution and ... - ACS Publications

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