Analysis and modeling of a silicon nitride slot-waveguide microring resonator biochemical sensor

Abstract

The performance of a recently demonstrated silicon nitride slot-waveguide microring resonator biochemical sensor is analyzed. The slot-waveguide sensor is optically modeled by using finite element method, full-vectorial and semivectorial finite-difference beam propagation methods. Numerical calculations are discussed and compared to the sensor experimental performance. This study includes homogeneous sensing -by using different aqueous solutions-, surface sensing -due to both, surface etching and biomolecular layer adhesion-, and power coupling characteristics of the microring sensor. It is found that all of the aforementioned numerical methods provide good agreement with the experimental homogeneous sensitivity, surface etching sensitivity and power transmission coefficient at the resonator coupling. The analysis of the surface sensitivity due to biomolecular layer adhesion suggests biomolecule polymerization on the surface of the actual device. These results demonstrate the suitability of the proposed numerical optical models and indicate that the slot-waveguide microring device can be fully wetted with aqueous analytes, which is desirable for sensing and optofluidic applications at the nanoscale.

Optical Slot-Waveguide Based Biochemical Sensors

“…This sensitivity is more than twice larger than that exhibited by biochemical sensors based on conventional-waveguide microrings [9,10], indicating that higher analyte-light interaction occurs in the slot-waveguide sensor. The device bulk sensitivity was also theoretically estimated [18] by using different numerical methods – to calculate the effective index variation (Δn eff ) of the ring optical mode as a function of Δn B – and the wavelength tuning relationship for microresonators: $\Delta {\lambda }_r=-\Delta n_{\mathit{\text{eff}}}\frac{2\pi }{{\lambda }_{r0}}\cdot {\left(-\frac{2\pi n_g({\lambda }_{r0})}{{\lambda }_{r0}^2}\right)}^{-1}={\lambda }_{r0}\frac{\Delta n_{\mathit{\text{eff}}}(\Delta n_B,\Delta {\lambda }_r)}{n_g({\lambda }_{r0})}$where n g (λ r0 ) is the group index, which is defined as n g = n eff - λ(∂λ/∂n eff ), at the unperturbed (Δn B = 0) resonance wavelength λ r0 . As seen in Figure 3, excellent agreement was found between the calculated and measured sensitivities [18,19], suggesting that the slot ring channel was fully filled with the different aqueous solutions employed in the measurements.…”

“…Black dots: experimental data (water-ethanol solutions) [12]; red line: finite element method (FEM) based calculations [18]. …”

Recent Advances in Integrated Photonic Sensors

“…For example, in case of glucose or ethanol detection, the optical waveguide is covered by an aqueous solution ( n c = 1.33 at λ = 1.55 μm), in which the analyte has to be dissolved. The dimensionless waveguide sensitivity, S h , can be evaluated as follows [14–16]: $S_h={\frac{\partial n_{\mathit{\text{eff}}}}{\partial n_c}|}_{n_c=n_c^0}=\frac{2n_c^0}{Z_{0}P}\underset{C}{\iint }{\left|\overrightarrow{E}(x,y)\right|}^2\mathit{\text{dxdy}}=\frac{2n_c^0{\iint }_{\infty }{\left|\overrightarrow{E}(x,y)\right|}^2\mathit{\text{dxdy}}}{Z_{0}P}{\mathrm{normal\Gamma }}_C^I$where: $P=\underset{\infty }{\iint }[(\overline{E}\times {\overline{H}}^{\ast }+{\overline{E}}^{\ast }\times \overline{H})\cdot \overline{z}]\mathit{\text{dxdy}}$…”

Surface sensing with integrated optical waveguides: a design guideline

“…Such sensors exhibit large potential for miniaturization and costefficient mass production, utilizing established photonic integration platforms such as silicon or silicon nitride. Sensor schemes are most commonly based on interferometers, e.g., in Mach-Zehnder and Young configuration [2,4,5, 10,11], or on resonant devices, such as ring, disk and Bragg resonators [1,2,5-9, [12][13][14][15][16][17], which can be further enhanced by exploiting the Vernier effect [18]. Enabling large effective interaction lengths with the analyte, these sensor structures combine high sensitivity with small device footprint and lend themselves to highdensity integration into massively parallel arrays.…”

“…Over the last years, various approaches for optimizing special types of WG were published, both for surface sensing [5,9,10,[15][16][17][18][19][20] and for detection of bulk refractive index changes in the WG cladding (homogeneous sensing) [1,2,6-9, [11][12][13][14][15][16][17][18][19][20][21][22]. However, these investigations are often limited to specific WG types and geometries on certain material platforms, such as silicon [1,2,5-9, [11][12][13][14]16,19,22], silicon nitride (Si3N4) [5,10,11,17,21] and polymers [11,19]. It is hence impossible to broadly compare the highest achievable surface sensitivities across different WG types and integration platforms.…”