Bibliographic record
Abstract
A silicon-based athermal double-ring resonator biosensor with a vertically coupled configuration is developed. We present an optimal design of the sensor structure by specifying the radii of the reference and the sensing rings, the vertical coupling offset, d, between the two rings and the bus waveguide, and the lateral offset, l, between the edges of the rings and the bus waveguide. By using Lumerical software package, we demonstrate that the optimal vertical and lateral offsets are d=325 nm and l=-80 nm, respectively. One major challenge faced by ring based biosensors is their temperature dependent characteristics. In this study, the sensing ring is exposed to the biomaterial under test, while the reference ring provides a temperature-insensitive reference to the sensing measurements. By assuming the biomaterial medium has small variations in temperature, we conclude that the proposed biosensor device offers temperature insensitive measurement, where the temperature effects are fully corrected by the reference ring response. The double-ring sensors are proposed to be fabricated with the local oxidation of silicon process, without the need for advanced lithography methods such as e-beam or deep UV lithography. In addition, the vertically coupled double-ring configuration allows precise control of the critical coupling separation between the rings and the bus waveguide. The proposed silicon double-ring biosensor can be used for highly sensitive and stable sensing for both biomedical and environmental applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".