Coherent Fiber-Optic Method for Spatially-Resolved, Time-Dependent, Picometer-Scale Displacement Amplitude Measurements of Surface Acoustic Waves
Bibliographic record
Abstract
Surface acoustic wave (SAW) devices are widely used in communications, sensing and quantum systems due to their small footprint and high sensitivity. Better understanding of SAW propagation theory and loss mechanisms could benefit device design for highly efficient SAW sensors. Here, we propose a technique for absoluteamplitude measurements of vertical SAW displacement, using a fiber point-sensor through optical-acoustic wave interaction. This is achieved by a curved Fabry-Pérot interferometer formed by a single-mode fiber endface and the curved SAW surface. A theoretical model is implemented to relate the sensor response to SAW amplitude and to eliminate the need for focusing lenses, all while maintaining high spatial resolution. The 4x larger SAW wavelength compared to the fiber endface dimension and the high optical sampling speed, ensure coherent optical and acoustic wave interaction. Results show amplitudes on the order of a few picometers for a lithium niobate-based SAW at 100 MHz, consistent with theoretical expectations and experimental validation. Through scanning the sensor in the horizontal direction, we can image the quasi-standing waves formed in the SAW cavity. The proposed setup is simpler, more compact, non-contact, applicable to more substrate materials compared to previous approaches, and can provide insights to improve SAW device design to enhance performance. It opens a new door for applications such as material surface defect detection, bio-sample concentration sensing, or coherent coupling to quantum systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".