Demonstrates accurate multiplexed diagnostics with fixed-wavelength lasers in biosensing, suggesting advancements in portable testing.
Decentralized diagnostic testing that is accurate, portable, quantitative, and capable of making multiple simultaneous measurements of different biomarkers at the point-of-need remains an important unmet need in the post-pandemic world. Resonator-based biosensors using silicon photonic integrated circuits are a promising technology to meet this need, as they can leverage (i) semiconductor manufacturing economies of scale, (ii) exquisite optical sensitivity, and (iii) the ability to integrate tens to hundreds of sensors on a millimeter-scale photonic chip. However, their application to decentralized testing has historically been limited by the expensive, bulky tunable lasers and alignment optics required for their readout. In this work, we realize, for the first time, a segmented sensor architecture that addresses this important challenge by facilitating resonance-tracking readout using a fixed-wavelength laser. The architecture incorporates an in-resonator phase shifter modulated by CMOS drivers to periodically sweep and acquire the resonance peak shifts, as well as a distinct high-sensitivity sensing region, maintaining high performance at a fraction of the cost and size. The segmented architecture comprises purpose-built resonator segments, with sensitivity dependent on the relative length and waveguide design in the sensing region, and improvements in quality factor and intrinsic limit of detection achievable using low-loss routing waveguides. We show, for the first time, that reading out the segmented sensor with a fixed-wavelength laser can offer similar performance to reading it out with the traditional tunable laser, demonstrating a system limit of detection of 6.1 ± 1.9 × 10−5 RIU as well as immunoassay-based detection of the SARS-CoV-2 spike protein. We anticipate that this sensor architecture will open the door to a new data-rich class of portable, accurate, multiplexed diagnostics for decentralized testing.
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Al-Qadasi et al. (2026) studied this question.
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