Enhanced Slow-Light Phenomenon in Dot-Bragg Gratings Through Offset Engineering
Bibliographic record
Abstract
We demonstrate enhanced slow-light phenomenon in dot-Bragg grating (DBGs) inscribed by femtosecond laser (FSL) in bulk glass through offset engineering. Two types of first-order DBGs, Model-I and Model-II, are inscribed based on single-laser-pass waveguide (SLPWG) and double-laser-pass waveguides (DLPWG), respectively. In Model-I DBG, the effective light-guiding occurs in the shell region with a refractive index (RI) change of ∼2×10⁻<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, while the core region exhibits a smaller RI change less than 0.4×10⁻<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>. The enhanced slow-light phenomena are achieved at a 5 μm offset, yielding a transmission dip of 6.7 dB, a slow-down factor (SDF) of 1.21, and a maximum group delay (GD) of 33 ps. In Model-II DBG, the two-pass inscription combined with center distance adjustment in DLPWG enhances the core RI change by a factor of 2 over SLPWG, with a value of 0.8×10⁻<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, while the shell RI change decreases to ∼1×10⁻<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> due to the lower repetition rate. With offset optimization, the transmission dip in Model-II DBG nearly doubles, up to 12.5 dB at a 5 μm offset with a 5 μm center distance, leading to slow-light effect with an SDF of 1.45 and a maximum GD of 43 ps—representing an 87% improvement in transmission, and 20% and 30% improvements in SDF and GD, respectively, compared to Model-I DBG. In addition, a time delay of ∼7 ps is obtained by comparing the detected signal pulse at the Bragg wavelength of Model-II DBG with the signal pulses at wavelengths tuned near the reflection edges, which represents an overestimated value for the slow-light effect. These results demonstrate that the two DBG models hold significant potential for optical filtering in communication and sensing, as well as in optical networks for data traffic management and dynamic reconfiguration of optical channels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".