Photonic Generation of Continuously Tunable Chirped Microwave Waveforms Based on a Temporal Interferometer Incorporating an Optically Pumped Linearly Chirped Fiber Bragg Grating
Bibliographic record
Abstract
A novel approach to generating a chirped microwave waveform with continuously tunable chirp rate based on a temporal interferometer incorporating an optically pumped linearly chirped fiber Bragg grating (LCFBG) is proposed and demonstrated. The temporal interferometer is realized using a Mach-Zehnder interferometer (MZI) that incorporates an LCFBG and a dispersion compensating fiber to generate a temporal interference pattern with an instantaneous frequency that is linearly proportional to time. A linearly chirped microwave waveform with its shape that is identical to the temporal interference pattern is generated at the output of a photodetector. The tuning of the chirp rate of the generated waveform is realized by optically pumping the LCFBG that is written in an erbium-ytterbium co-doped fiber with different pumping powers. The key advantage of using optical pumping over external thermal tuning or mechanical tuning to tune the dispersion of the LCFBG is that the dispersion can be tuned at a high speed and controlled remotely. Moreover, the undesirable birefringence effects existing in the mechanical tuning technique can also be avoided. A theoretical analysis is performed that is verified by numerical simulations and an experiment. A linearly chirped microwave waveform with a tunable chirp rate from 79 to 64 GHz/ns by changing the injection current to the pumping laser diode from 0 to 100 mA is generated. The experimental results also show that the central frequency of the generated chirped microwave waveform can be changed by tuning the longitudinal offset of the MZI.
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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 | 0.000 | 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".