Coherent silicon photonic interferometric biosensor with an inexpensive laser source for sensitive label-free immunoassays

Leuermann, Jonas; Stamenkovic, Vladimir; Ramirez-Priego, Patricia; Sánchez‐Postigo, Alejandro; Gavela, Adrián Fernández; Chapman, Cole A.; Bailey, Ryan C.; Lechuga, Laura M.; Pérez‐Inestrosa, Ezequiel; Collado, Daniel; Halir, Robert; Molina-Fernández, Í.

doi:10.1364/ol.411635

Cited by 16 publications

(8 citation statements)

References 36 publications

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“…The reduction in losses improves the reliability and sensitivity of biosensing measurements. 15 After determining the width and height data of the waveguides, the next step is to measure the refractive index for both the SiO 2 and Si 3 N 4 layers, which are dependent on the wavelength. These layers are elaborated on in greater detail in section 3.…”

Section: Design and Layoutmentioning

confidence: 99%

Ab-initio modeling of bend-losses in silicon nitride waveguides from visible to near-infrared

Hinum-Wagner,

Hörmann,

Sattelkow

et al. 2023

Optical Modeling and Performance Predictions XIII

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Integrated photonics features applications in high-speed telecommunication, computing, and sensing. These devices are ultimately limited by the optical loss occurring in the waveguide structures. One of its primary sources is surface-roughness-induced scattering and bend-losses. Surface roughness is unavoidably introduced during deposition, mainly during etching and lithographic steps. In photonic integrated circuits, tight bends enable a compact footprint yet increase the mode mismatch loss , radiation loss and scattering loss. Previously, the bend losses were estimated from a parametric model. However, it lacks flexibility w.r.t. the waveguide platform. We apply a recently developed model of the surface-roughness-induced scattering in guided-mode systems to substantiate the dependence of the scattering loss on the bend-radius for waveguides based on a silicon nitride platform. The model incorporates the surface roughness via its autocorrelation. Further, it inherently considers the overlap of the modes with the roughness. As waveguide material, we used both plasma-enhanced CVD silicon nitride as a low-temperature, back-end-compatible process, and low-pressure CVD silicon nitride, as a high-temperature frontend process. As bottom and top cladding, we deposited high-density plasma (HDP) and sputtered silicon oxide, respectively. The latter offers flexibility to adapt the platform for sensing purposes. We evaluate different waveguide widths, bend radii, and wavelengths in the visible and near-infrared ranges. We set the observed propagation losses into context with estimated absorption, scattering, and mode-overlap loss sources and point to their shifting importance at the measured wavelengths. We believe that this model allows to increase our knowledge about the various aspects of loss in guided mode systems and predict the propagation loss based on foregoing absorption and roughness measurements.

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Section: Design and Layoutmentioning

confidence: 99%

Ab-initio modeling of bend-losses in silicon nitride waveguides from visible to near-infrared

Hinum-Wagner,

Hörmann,

Sattelkow

et al. 2023

Optical Modeling and Performance Predictions XIII

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“…Most of the photonic integrated sensors employ the concept of evanescent field detection, where the analyte adheres to a bioreaction layer on the surface of the wave guide and interacts with the evanescent field of the guided wave [ 62 ]. The initial displacement in particular biosensors may be increased by about four orders of magnitude by utilizing preselection to choose the polarization and postselection to create destructive interference [ 63 ]. This signal enhancement approach can simplify the optical components and lower the cost of the sensor device in addition to measuring the spin-dependent splitting in biosensors [ 64 ].…”

Section: Sars-cov-2 Biosensorsmentioning

confidence: 99%

“…Furthermore, due to its distinct optical characteristics, the photonic spin Hall effect has generated a lot of study in recent years [ 62 ]. On-chip resonant or interferometric devices are used to translate changes in the optical phase, which cannot be detected directly, into changes in the optical power [ 63 ].…”

Section: Sars-cov-2 Biosensorsmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

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“…For example, the light source is typically a tunable laser, which is expensive and not suitable for miniaturized systems. To overcome this issue, Leuermann et al have developed a coherent light detection approach using a single wavelength laser source [18]. Besides that, the realization of a pluggable sensor solution, which needs to be simple and cost-effective, is challenging due to the required alignment tolerances between optical fiber and chip.…”

Section: Route For Commercialization Of Spri and Pic Devicesmentioning

confidence: 99%

Surface Plasmon Resonance Imaging (SPRi) and Photonic Integrated Circuits (PIC) for COVID-19 Severity Monitoring

Steglich

Schasfoort

2022

COVID

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Direct optical detection methods such as surface plasmon resonance imaging (SPRi) and photonic-integrated-circuits (PIC)-based biosensors provide a fast label-free detection of COVID-19 antibodies in real-time. Each technology, i.e., SPRi and PIC, has advantages and disadvantages in terms of throughput, miniaturization, multiplexing, system integration, and cost-effective mass production. However, both technologies share similarities in terms of sensing mechanism and both can be used as high-content diagnostics at or near to point of care, where the analyte is not just quantified but comprehensively characterized. This is significant because recent results suggest that not only the antibody concentration of the three isotypes IgM, IgG, and IgA but also the strength of binding (affinity) gives an indication of potential COVID-19 severity. COVID-19 patients with high titers of low affinity antibodies are associated with disease severity. In this perspective, we provide some insights into how SPR and PIC technologies can be effectively combined and complementarily used for a comprehensive COVID-19 severity monitoring. This opens a route toward an immediate therapy decision to provide patients a treatment in an early stage of the infection, which could drastically lowers the risk of a severe disease course.

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Coherent silicon photonic interferometric biosensor with an inexpensive laser source for sensitive label-free immunoassays

Cited by 16 publications

References 36 publications

Ab-initio modeling of bend-losses in silicon nitride waveguides from visible to near-infrared

Ab-initio modeling of bend-losses in silicon nitride waveguides from visible to near-infrared

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

Surface Plasmon Resonance Imaging (SPRi) and Photonic Integrated Circuits (PIC) for COVID-19 Severity Monitoring

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