Performance of Grating Couplers Used in the Optical Switch Configuration
Bibliographic record
Abstract
Surface plasmon resonance is an effect widely used for biosensing. Biosensors based on this effect operate in different configurations, including the use of diffraction gratings as couplers. Gratings are highly tunable and are easy to integrate into a fluidic system due to their planar configuration. We discuss the optimization of plasmonic grating couplers for use in a specific sensor configuration based on the optical switch. These gratings present a sinusoidal profile with a high depth/period ratio. Their interaction with a p-polarized light beam results in two significant diffracted orders (the 0th and the −1st), which enable differential measurements cancelling noise due to common fluctuations. The gratings are fabricated by combining laser interference lithography with nanoimprinting in a process that is aligned with the challenges of low-cost mass production. The effects of different grating parameters such as the period, depth and profile are theoretically and experimentally investigated.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".