On-chip dispersive phase filters for optical processing of periodic signals
Bibliographic record
Abstract
We develop a dispersive phase filter design framework suitable for compact integration using waveguide Bragg gratings (WBGs) in silicon. Our proposal is to utilize an equivalent “discrete” spectral phase filtering process, in which the original continuous quadratic spectral phase function of a group velocity dispersion (GVD) line is discretized and bounded in a modulo <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>2</mml:mn> <mml:mi>π</mml:mi> </mml:math> basis. Through this strategy, we avoid the phase accumulation of the GVD line, leading to a significant reduction in device footprint (length) as compared to conventional GVD devices (e.g., using a linearly chirped WBG). The proposed design is validated through numerical simulations and proof-of-concept experiments. Specifically, using the proposed methodology, we demonstrate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> </mml:math> pulse repetition-rate multiplication of a 10 GHz picosecond pulse train by dispersion-induced Talbot effect on a silicon chip.
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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) | 0.000 | 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".