Optimization of integrated optical ring microresonator structure for sensitive absorption spectroscopy (Conference Presentation)
Bibliographic record
Abstract
Optical ring micro-resonators (OMR) can be integrated onto chips to obtain sensitive, robust, low cost and portable sensor systems. They are used for in-situ real time detection of specific molecules by specialized or non- specialized persons. Target analytes, homogeneously spread in the cladding layer, induces a complex refractive index variation Δncl of the OMR waveguides upper cladding. In this study, we propose an optimized analytical approach to OMR designs in terms of bulk sensitivity. Those type of sensors are based on the evanescent field sensing. Interaction between the evanescent field and the analytes induces resonance wavelengths modifications. The main sensing strategy is based on resonant wavelength shift measurement. However, contrast variation, due to the absorption coefficient linked to analytes concentration, can also be measured. Colorimetric reactions, used to obtain a specific sensor, change significantly the light intensity in a specific peak of the transmission spectrum. This is due to the complex formation between a specific ligand and a heavy metal, such as hexavalent chromium and 1,5 diphenylcarbazide. From the well-known ring resonator’s transmission expression, we can establish an analytical model of sensitivity’s dependence on geometric dimensions. Sensitivity in influenced by the round-trip attenuation coefficient a, the auto-coupling coefficient τ, the optical path and the ratio of guided power into the cladding Γcl. We validated our approach with FDTD simulation of OMR’s response for a 15 μm radius. This analytical approach makes it possible, from the waveguide propagation structure and propagation losses, to obtain both the optimal ring radius and the resonator gap in order to obtain maximum sensitivity. Based on optical characterization of OMR, measured variations of 1% power drop at resonance should allow variation measurement on the extinction coefficient of ∆ni = 10−6.
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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) | 0.001 | 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 | 0.001 |
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".