Enhancing the resonance stability of a high-Q micro/nanoresonator by an optical means
Bibliographic record
Abstract
High-quality optical resonators underlie many important applications ranging from optical frequency metrology, precision measurement, nonlinear/quantum photonics, to diverse sensing such as detecting single biomolecule, electromagnetic field, mechanical acceleration/rotation, among many others. All these applications rely essentially on the stability of optical resonances, which, however, is ultimately limited by the fundamental thermal fluctuations of the devices. The resulting thermo-refractive and thermo-elastic noises have been widely accepted for nearly two decades as the fundamental thermodynamic limit of an optical resonator, limiting its resonance uncertainty to a magnitude 10-12 at room temperature. Here we report a novel approach that is able to significantly improve the resonance stability of an optical resonator. We show that, in contrast to the common belief, the fundamental temperature fluctuations of a high-Q micro/nanoresonator can be suppressed remarkably by pure optical means without cooling the device temperature, which we term as temperature squeezing. An optical wave with only a fairly moderate power launched into the device is able to produce strong photothermal backaction that dramatically suppresses the spectral intensity of temperature fluctuations by five orders of magnitudes and squeezes the overall level (root-mean-square value) of temperature fluctuations by two orders of magnitude. The proposed approach is universally applicable to various micro/nanoresonator platforms and the optimal temperature squeezing can be achieved with an optical Q around 106-107 that is readily available in various current devices. The proposed photothermal temperature squeezing is expected to have profound impact on broad applications of high-Q cavities in sensing, metrology, and integrated nonlinear/quantum photonics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".