Do you need a tunable laser for resonant cavity optical sensors?
Bibliographic record
Abstract
By measuring the shift in the resonant frequency of whispering gallery modes in an optical microcavity, it is possible to obtain very high sensitivity to changes in the properties of the surrounding medium and the sensor surface. However, a narrow linewidth tunable laser is typically required in order to track the frequency shift. This significantly increases the cost and complexity of such systems. Phase shift cavity ring down spectroscopy (PS- CRDS) represents an alternative approach. In PS-CRDS the interrogating optical signal is sinusoidally modulated and the shift in the phase of the detected signal (rather than the shift in the cavity resonant wavelength) provides information about changes in the cavity properties. PS-CRDS has previously been successfully implemented in resonant optical microcavities, but a tunable laser was still used in order to maintain coupling to the cavity resonance. Here we consider the use of a broadband optical source (e.g. a diode laser or LED) to interrogate the cavity using the PS-CRDS principle. The spectrum of the source always spans more than one cavity resonance and so does not need to be tuned as the cavity resonances shift. We undertake an analytical and experimental investigation to evaluate the effectiveness of this approach and compare it to traditional interrogation methods in terms of sensitivity and signal-to-noise ratio. We focus in particular on the implementation of a resonant cavity biosensor in silicon photonics ring resonators. The results of the study show that the sensitivity to changes in cavity mode loss is slightly lower than when a narrow linewidth source is used, and that sensitivity to changes in the effective refractive index is very significantly reduced. We will discuss the implications of these results in terms of suitable applications of this technique, and the improved potential for integration that the low coherence source brings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".