Tunable Subterahertz Wave Generation Based on Photonic Frequency Sextupling Using a Polarization Modulator and a Wavelength-Fixed Notch Filter
Bibliographic record
Abstract
Optical frequency multiplication based on electrooptical modulation is an effective way to generate high-spectral-purity and frequency-tunable subterahertz waves. The previously demonstrated frequency-doubling and quadrupling techniques based on a Mach-Zehnder modulator have a low multiplication factor and suffer from bias drift problem and residual chirp. In this paper, a novel approach to achieving frequency sextupling using a polarization modulator and a wavelength-fixed optical notch filter is proposed and experimentally demonstrated. The method is free from bias drift problem and residual chirp, which can be used to generate high-spectral-purity subterahertz wave signals using relatively low-frequency electrical and optical devices. By using a narrow-bandwidth fiber Bragg grating as a wavelength-fixed optical notch filter, a high-spectral-purity microwave signal tunable from 18 to 27.6 GHz is generated when a microwave drive signal from 3 to 4.6 GHz is applied to the polarization modulator. The phase noise of the generated signal is measured as low as -107.57 dBc/Hz at a 10-kHz offset frequency. By replacing the narrow-bandwidth notch filter by an optical interleaver, a subterahertz wave tunable from 66 to 114 GHz is generated when the drive signal is tuned from 11 to 19 GHz. The distribution of the generated signal over optical fiber is investigated. The results show that the quality of the distributed subterahertz wave signal is maintained after transmission over a 40-km standard single-mode fiber.
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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".