A Computational Approach to Optimize Microring Resonators for Biosensing Applications

Justin C. Wirth, Birck Nanotechnology Center, Purdue University
B. R. Wenner, Birck Nanotechnology Center, Purdue University
M. S. Allen, Birck Nanotechnology Center, Purdue University
J. W. Allen, Birck Nanotechnology Center, Purdue University
M. Qi, Birck Nanotechnology Center, Purdue University

Date of this Version



DOI: 10.1117/12.2001820


Microcavity structures have recently found utility in chemical/biological sensing applications. The appeal of these structures over other refractive index-based sensing schemes, such as those based on surface plasmon resonance, lies in their potential for producing a highly sensitive response to binding events. High-Q devices, characterized by sharp line widths, are extremely attractive for sensing applications because the bound analyte provides an increased optical pathlength, thus shifting the resonant frequency of the device. In this work, we design and simulate resonant microrings using full-wave finite element models. In addition to structure design, integration of the biological recognition element on the resonator is also considered. This is equally important in dictating the sensitivity of the sensing device. To this end, we take a four-step theoretical approach to optimizing the sensor. We begin by using FEM analysis to obtain the characteristic resonant wavelength, line width, and quality factor for bare ring resonators absent of surface functionalization. Next, we simulate the structure with a biorecognition element attached to the surface. The third step is to model the functionalized microring to mimic the interaction with the target analyte. At each step, we derive the transmission spectra, electric field distributions and coupling efficiencies, as well as wavelength dependence using empirical data for the refractive indices of biorecognition element and analyte. Finally, the geometry of the microrings is optimized in conjunction with the constituent material properties and the recognition chemistry using FEM combined with an optimization algorithm to maximize the sensitivity of the integrated biosensor.


Nanoscience and Nanotechnology