Dataset for "Stimulated Forward Brillouin Scattering in Subwavelength Grating Silicon Membranes"
Bibliographic record
Abstract
ABSTRACT Brillouin scattering enables efficient and coherent conversion between optical photons and gigahertz‐frequency phonons. Implementing this interaction in silicon photonics provides a pathway toward scalable and cost‐effective devices, leveraging mature fabrication processes. However, achieving strong overlap and tight confinement of optical and mechanical modes in silicon nanophotonic waveguides remains a significant challenge. Here, we propose and demonstrate a novel strategy that enables independent control of optical and mechanical modes in periodically segmented silicon waveguides. Our approach combines two distinct periodic lattices: one with a period shorter than half of the optical wavelength, providing light guiding by metamaterial‐induced index contrast, and another that creates a complete phononic bandgap confining acoustic modes. This dual‐lattice strategy opens new degrees of freedom to optimize optomechanical confinement and coupling simultaneously. Based on this approach, we experimentally demonstrate remarkably high Brillouin gain of , resulting in a three‐tone Stokes gain of 3 and anti‐Stokes loss of 4 with 6.4 MHz mechanical linewidth. These results illustrate the potential of subwavelength silicon metamaterials for engineering on‐chip optomechanical interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.277 | 0.060 |
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".