Sensing ultrasound optically with photonic crystal slabs: a tale of two mechanisms (Conference Presentation)
Bibliographic record
Abstract
Silicon Photonics-based sensors can provide low cost, high sensitivity optical detection solutions in ultrasound and photoacoustic (PA) imaging. We demonstrate experimentally the measurement of ultrasound (2.5-8.5 MHz) in water using photonic crystal slab (PCS) nanostructure devices. Each PCS is composed of a periodic array of nanoholes, etched into silicon nitride (t=160 nm), on top of a silicon dioxide layer, and silicon substrate. The PCS devices have guided resonances that peak at ~ 1550 nm, with linewidths that vary from 0.7 to 5.5 nm. One type of PCS device includes a PCS nanostructure located above a thin micro-fabricated silicon membrane (~ 10 micron thick). Membrane deformation by incoming ultrasound waves induce resonance changes in the PCS spectral peak location (i.e., drum effect). We observe these drum-effect PCS devices to have acoustic sensitivities that are very narrowband (with bandwidths ~ 1 MHz), with a 300-micron diameter drum device found to have a peak sensitivity at 5 MHz and a noise equivalent pressure (NEP) of 2.0 kPa (72 Pa/rt Hz). In another mechanism, the sensitivity of the PCS nanostructures to changes in the ambient index of refraction is used. A pressure wave in water that impinges the PCS is accompanied by changes in the water's index of refraction, which causes the resonance peak of the PCS to shift. The acoustic sensitivities of these PCS devices is found to be broadband (> 6 MHz), in contrast to the drum-effect devices, with an NEP of less than 0.5 kPa (6.7 Pa/rt Hz). These devices can potentially allow for optics-based monolithic ultrasound sensor arrays, optimized for PA 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 | 0.001 | 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".