Improving the performance of silicon photonic optical resonator-based sensors for biomedical applications
Bibliographic record
Abstract
Silicon photonic biosensors show great potential for applications in medical diagnostics and healthcare services. Near-infrared transparency and high refractive index of silicon allow us to build compact and efficient circuits leveraging CMOS foundries, which provide low-cost mass production and enable the integration of the optoelectronic components on the same chip. Although silicon photonic biosensors have proven performances close to today's gold standard diagnostics, many applications still require higher multiplexing, as well as more sensitive, reliable and quantitative measurements. This dissertation is based on theoretical and experimental studies of silicon photonic sensing architectures in terms of sensor performance improvement and unit-cost reduction. Specifically, two novel sub-wavelength grating-based (SWG) waveguide configurations are presented to improve the sensitivity. Leveraging the advantage of SWG metamaterials, the substrate-overetch (SOE) and multi-box SWG devices present a largely extended modal size and surface contact area, which gives 10-time enhanced sensitivity compared to the conventional devices. In addition, by employing the Bragg grating as the sensing architecture, the multi-box SWG-based grating configuration achieves a lower detection limit compared to the microring resonator (MRR) counterpart and demonstrates the capability for monitoring small molecule interactions. Replacing the laser with a broadband source can provide a lower-cost solution for the optical system. Therefore, two cost-effective broadband light source-based sensing implementations are proposed and demonstrated with acceptable sensitivities. The first implementation uses cascaded MRRs for index monitoring, where the analyte variation is converted to the photocurrent change as the readout. The second implementation uses a phase-shifted Bragg grating-based symmetrical Mach-Zehnder interferometer, where the analyte variation maps the intensity change at the resonant wavelength. Furthermore, a system-level integration of active silicon photonic sensors using Fan-Out Wafer-Level-Packaging (FOWLP) is proposed in the dissertation, which can reduce the die size down to 1 mm² while simplifying the microfluidic and optical integration. Leveraging the CMOS foundries and the proposed FOWLP technique, the unit cost of each packaged sensing die can be reduced to several dollars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".