Porosity calibration in a 4-layer porous silicon structure to fabricate a micro-resonator with well-defined refractive indices and dedicated to biosensing applications
Source abstract
Abstract
Abstract Calibration of the porosity as well as the electrochemical anodization rate in a 4-layer porous silicon (PSi) waveguide structure, including a guiding, a confinement and two technological barrier layers, is studied and validated. The objective is to process using standard photolithography a 4-layer PSi micro-resonator (MR) that will be butt-coupled with polymer waveguide and used for biosensing applications. The knowledge of the porosity and thus the refractive index of each PSi layer is then crucial to fabricate the hybrid optical component. The presence of the two PSi barrier layers is necessary to prevent the infiltration of polymers or resin during the process but influences the porosity and the anodization rate of both the guiding and the confinement PSi layers. Their porosity is lower than if these layers had been manufactured separately (one layer only) and the anodization rate is on the contrary higher. Taking into consideration the calibration results to obtain the target thicknesses and refractive indices of the guiding and the confinement PSi layers respectively, a MR based on the 4-layer PSi structure is fabricated. The PSi MR experimental transmission spectrum well corresponds to the simulated one which has been calculated from porosity calibration results and from experimental dimensions of the component measured by scanning electron microscope observations. Finally, a promising theoretical surface sensitivity of 0.06 nm/(pg/mm2) for Bovin Serum Albumin detection with a limit of detection of 0.1 pg/mm2 has been calculated for the PSi MR.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Silicon Nanostructures and Photoluminescence · Photonic and Optical Devices · Nanowire Synthesis and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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