Photonic Generation of Linearly Chirped Microwave Waveforms Using a Silicon-Based On-Chip Spectral Shaper Incorporating Two Linearly Chirped Waveguide Bragg Gratings
Bibliographic record
Abstract
Photonic generation of linearly chirped microwave waveforms using a silicon-based on-chip spectral shaper is proposed and demonstrated. The on-chip optical spectral shaper has a Mach-Zehnder interferometer (MZI) structure, in which two identical linearly chirped waveguide gratings (LC-WBGs) with opposite chirp rates are incorporated into the two arms. The LC-WBGs are fabricated on two rib waveguides by linearly varying the rib widths along the gratings to produce linear chirps. By adding an offset waveguide to one arm of the MZI and controlling the length of the offset waveguide, the spectral response of the shaper can be controlled to have a symmetrical, linearly increasing, or linearly decreasing free spectral range, which is needed for the generation of a linearly chirped microwave waveform based on spectral-shaping and wavelength-to-time mapping. The proposed device is fabricated using a CMOS-compatible process with 193-nm deep ultraviolet lithography. A theoretical analysis on the chirped microwave waveform generation is performed, which is verified by an experiment. Two linearly chirped microwave waveforms with symmetrical and uniform chirp profiles are experimentally generated using two fabricated spectral shapers with different lengths of the offset waveguides.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".