Department of Electrical and Computer Engineering, Interdisciplinary Institute for Technological Innovation (3IT), Université de Sherbrooke, Sherbrooke, Qué., Canada
Author bio:
Correspondence: Professor JJ Dubowski, Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, Qué. J1K 2R1, Canada URL: http://www.dubowski.ca
The surface plasmon resonance tracking over metal surfaces is a well-established
commercially available
biochemical quantification tool primarily applied in research. The utilization of such a tool is
however
constrained to highly specialized industries
capable of justifying the human and instrumental resource investments required by the characterization method. We have proposed to expand the field of application of this biosensing approach by redesigning this method through the integration and miniaturization within a semiconductor platform. Uncollimated and broadband emission from a light-emitting semiconductor is employed to couple a continuum of surface plasmon modes over a metal–dielectric architecture interfaced with a GaAs–AlGaAs substrate. A tensor version of rigorous coupled wave theory is employed to optimize the various fabrication specifications and to predict the light scatterings over a wide range of variables. We then present a hyperspectral characterization microscope capable of directly mapping the dispersion relation of scattered light
including diffracted surface plasmons
as an intensity distribution versus photon energy and surface wavevectors. Measurements carried out in a buffered solution demonstrate the accurate description of the uncollimated and broadband surface plasmon states. Finally
we introduce a simplified method of dispersion mapping
in which quasi-conic cross-sections of the light's scattering can be acquired directly
thus monitoring surficial responses in as fast as 1.2 s. This is over 300 times faster than required by implementing full dispersion mapping. While compromising on the volume of collected information
this method
combined with the solid-state integration of the platform
shows great promise for the fast detection of biochemical agents.
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