Design of Slow-Light Subwavelength Grating Waveguides for Enhanced On-Chip Methane Sensing by Absorption Spectroscopy
Bibliographic record
Abstract
The performance of on-chip gas sensors using absorption spectroscopy are currently limited by the small overlap and reduced interaction length between the light and the analyte. Here, the use of slow-light in subwavelength grating (SWG) waveguide integrated on a silicon photonic chip is proposed to improve methane sensing by tunable diode laser absorption spectroscopy in the near infrared. Such SWG waveguide increases the interaction by two means. First, close to the photonic bandgap edge, a SWG waveguide no longer acts as a metamaterial with a homogeneous index, but rather as a 1D photonic crystal in which slow-light effect enhances the light-analyte interaction. Second, the subwavelength segmentation of the waveguide increases the modal overlap with the air. These two enhancement mechanisms results in a six-fold improvement of the interaction with respect to strip waveguides. In this paper, we discuss how to engineer the group index of SWG waveguides to exploit slow-light effect for the first time. Design guidelines for minimizing propagation loss and disorder effect are discussed considering limitations of typical fabrication processes. SWG waveguides could improve the sensitivity and the limit of detection of on-chip trace-gas sensors that provide a compact, fabrication tolerant, inexpensive, and selective sensing technology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".