Optimized sensitivity of Silicon-on-Insulator (SOI) strip waveguide resonator sensor
Bibliographic record
Abstract
Evanescent field sensors have shown promise for biological sensing applications. In particular, Silicon-on-Insulator (SOI)-nano-photonic based resonator sensors have many advantages for lab-on-chip diagnostics, including high sensitivity for molecular detection and compatibility with CMOS foundries for high volume manufacturing. We have investigated the optimum design parameters within the fabrication constraints of Multi-Project Wafer (MPW) foundries that result in the highest sensitivity for a resonator sensor. We have demonstrated the optimum waveguide thickness needed to achieve the maximum bulk sensitivity with SOI-based resonator sensors to be 165 nm using the quasi-TM guided mode. The closest thickness offered by MPW foundry services is 150 nm. Therefore, resonators with 150 nm thick silicon waveguides were fabricated resulting in sensitivities as high as 270 nm/RIU, whereas a similar resonator sensor with a 220 nm thick waveguide demonstrated sensitivities of approximately 200 nm/RIU.
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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 | 0.000 | 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".