Design of Ultra-Compact On-Chip Discrete Phase Filters for Broadband Dispersion Management
Bibliographic record
Abstract
This paper provides an in-depth theoretical and numerical analysis of a group-velocity dispersion (GVD) management scheme for broadband waveforms using ultra-compact on-chip discrete spectral phase filters based upon waveguide Bragg gratings (WBGs) in a silicon-on-insulator (SOI) platform. Through this technique, mm-long discrete phase filters can be designed to impart a target arbitrary GVD profile on a high-rate (typically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\geq\!10$</tex-math></inline-formula> GHz) periodic pulsed waveform, including fully customized and extremely large second and higher-order dispersion terms (e.g., equivalent to 10,000 km of a standard single-mode fiber), over a broad frequency bandwidth (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim\!3$</tex-math></inline-formula> THz, demonstrated here). The capabilities and limitations of this technique to impart a target GVD profile over an arbitrary (generally, non-periodic) broadband signal are also studied. We show that customized GVD lines can be efficiently implemented offering a net group-delay excursion in the hundreds-of-ps range using mm-long integrated discrete phase filters. Additionally, we suggest and numerically demonstrate a simple and practical strategy to improve significantly the performance of the discrete phase filtering approach for application on non-periodic waveforms, by combining the discrete phase filter with a suitable periodic resonance (frequency-comb) amplitude filter. An extensive tolerance analysis is conducted, and we conclude that the proposed SOI design framework is well within practical fabrication requirements as well as robust to the expected variability in the main WBG device design parameters.
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 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) | 0.000 | 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 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".