Ultrasound sensing at thermomechanical limits with optomechanical buckled-dome microcavities
Bibliographic record
Abstract
We describe the use of monolithic, buckled-dome cavities as ultrasound sensors. Patterned delamination within a compressively stressed thin film stack produces high-finesse plano-concave optical resonators with sealed and empty cavity regions. The buckled mirror also functions as a flexible membrane, highly responsive to changes in external pressure. Owing to their efficient opto-acousto-mechanical coupling, thermal-displacement-noise limited sensitivity is achieved at low optical interrogation powers and for modest optical ( Q ∼ 10 3 ) and mechanical ( Q ∼ 10 2 ) quality factors. We predict and verify broadband (up to ∼ 5 MHz), air-coupled ultrasound detection with noise-equivalent pressure (NEP) as low as ∼ 30-100 µPa/Hz 1/2 . This corresponds to an ultrasonic force sensitivity ∼ 2 × 10 −13 N/Hz 1/2 and enables the detection of MHz-range signals propagated over distances as large as ∼ 20 cm in air. In water, thermal-noise-limited sensitivity is demonstrated over a wide frequency range (up to ∼ 30 MHz), with NEP as low as ∼ 100-800 µPa/Hz 1/2 . These cavities exhibit a nearly omnidirectional response, while being ∼ 3-4 orders of magnitude more sensitive than piezoelectric devices of similar size. Easily realized as large arrays and naturally suited to direct coupling by free-space beams or optical fibers, they offer significant practical advantages over competing optical devices, and thus could be of interest for several emerging applications in medical and industrial ultrasound imaging.
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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 | 0.000 | 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".