Silicon photonic resonator sensors and devices
Bibliographic record
Abstract
Silicon photonic resonators, implemented using silicon-on-insulator substrates, are promising for numerous applications. The most commonly studied resonators are ring/racetrack resonators. We have fabricated these and other resonators including disk resonators, waveguide-grating resonators, ring resonator reflectors, contra-directional grating-coupler ring resonators, and racetrack-based multiplexer/demultiplexers. While numerous resonators have been demonstrated for sensing purposes, it remains unclear as to which structures provide the highest sensitivity and best limit of detection; for example, disc resonators and slot-waveguide-based ring resonators have been conjectured to provide an improved limit of detection. Here, we compare various resonators in terms of sensor metrics for label-free bio-sensing in a micro-fluidic environment. We have integrated resonator arrays with PDMS micro-fluidics for real-time detection of biomolecules in experiments such as antigen-antibody binding reaction experiments using Human Factor IX proteins. Numerous resonators are fabricated on the same wafer and experimentally compared. We identify that, while evanescent-field sensors all operate on the principle that the analyte's refractive index shifts the resonant frequency, there are important differences between implementations that lie in the relationship between the optical field overlap with the analyte and the relative contributions of the various loss mechanisms. The chips were fabricated in the context of the CMC-UBC Silicon Nanophotonics Fabrication course and workshop. This yearlong, design-based, graduate training program is offered to students from across Canada and, over the last four years, has attracted participants from nearly every Canadian university involved in photonics research. The course takes students through a full design cycle of a photonic circuit, including theory, modelling, design, and experimentation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".