Optically tunable single passband microwave photonic filter based on phase-modulation to intensity-modulation conversion in a silicon-on-insulator microring resonator
Bibliographic record
Abstract
We propose and experimentally demonstrate a single passband frequency-tunable microwave photonic filter (MPF) based on phase-modulation to intensity-modulation conversion in an optically pumped silicon-on-insulator (SOI) microring resonator (MRR). In the proposed MPF, a phase-modulated optical signal is filtered by an SOI MRR, to have one first-order sideband suppressed by the MRR notch. The phase-modulated optical signal is converted to an intensity-modulated single-sideband (SSB) signal and detected at a photodetector (PD). The entire operation is equivalent to a single passband MPF with the central frequency determined by the wavelength difference between the optical carrier and the MRR notch. The frequency tunability is achieved by tuning the resonance wavelength of the MRR, which is realized by optically pumping the MRR using an optical pumping source with its wavelength located at one resonant wavelength of the MRR. The thermal effect due to the free carrier absorption (FCA) and free carrier dispersion (FCD) resulted from the two photon absorption (TPA) in the MRR leads to a net redshift in the resonance wavelength, enabling the tuning of the frequency of the MPF. The approach is experimentally evaluated. An all-optically tunable single passband MPF with a tunable frequency from 16 to 23 GHz is experimentally demonstrated.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".