Offset-enhanced slow light in femtosecond laser-fabricated Bragg gratings
Bibliographic record
Abstract
We report a strength-enhanced waveguide second-order line-Bragg grating (WLBG) directly written with femtosecond laser in bulk glass by using “offset” to exploit the slow-light effect. This design eschews the use of multiple waveguides and/or waveguide bundles for light guiding. Instead, it only employs a single-laser-pass waveguide (SLPWG) with a refractive index change of 1.1 × 10−3, to achieve effective light propagation. The SLPWG is first written as a core-shell ellipsoid unit by a single-laser pass. Subsequently, a line-grating is written on top, with an offset to accommodate for the already modified refractive index from the waveguide along the vertical direction of different offset values 0 µm, 5 µm, 10 µm, and 15 µm. The enhanced slow-light effect for WLBG is studied theoretically and experimentally. Optimal performance occurs at a 10 µm offset, exhibiting a maximum group delay of 35 ps and a derived slow-down factor (SDF) of up to 1.54, with a 12.5 dB transmission dip and a propagation loss of 1.16 dB/cm, in vertical polarization. The experimental SDF results demonstrate the potential of our design for future applications in creating slow-wave structures via grating dispersion for compact photonic integrated devices, applying it to microfluid devices that can increase the light-liquid interaction path for the detection of refractive index change caused by variations in fluid concentration and composition, directly incorporating it into the hardened glass of cellphone screens for embedded sensors, as well as integrating it into optical antennas within smart glass windows that can enhance light-matter interactions for enabling real-time monitoring of environmental changes and improving wireless communications.
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 | 0.000 | 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".