Optical racetrack resonator transduction of nanomechanical cantilevers
Bibliographic record
Abstract
Optomechanical transduction has demonstrated its supremacy in probing nanomechanical displacements. In order to apply nano-optomechanical systems (NOMS) as force and mass sensors, knowledge about the transduction responsivity (i.e. the change in measured optical transmission with nanomechanical displacement) and its tradeoffs with system design is paramount. We compare the measured responsivities of NOMS devices with varying length, optomechanical coupling strength gom, and optical cavity properties. Cantilever beams 1.5 to 5 μm long are fabricated 70 to 160 nm from a racetrack resonator optical cavity and their thermomechanical (TM) noise signals are measured. We derive a generic expression for the transduction responsivity of the NOMS in terms of optical and mechanical system parameters such as finesse, optomechanical coupling constant, and interaction length. The form of the expression holds direct insight as to how these parameters affect the responsivity. With this expression, we obtain the optomechanical coupling constants using only measurements of the TM noise power spectra and optical cavity transmission slopes. All optical pump/probe operation is also demonstrated in our side-coupled cantilever-racetrack NOMS. Finally, to assess potential operation in a gas sensing environment, the TM noise signal of a device is measured at atmospheric pressure.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".