High-Speed and High-Resolution Interrogation of a Silicon Photonic Microdisk Sensor Based on Microwave Photonic Filtering
Bibliographic record
Abstract
High-speed and high-resolution interrogation of a silicon photonic microdisk sensor based on microwave photonic filtering and advanced signal processing is proposed and experimentally demonstrated. An integrated microdisk resonator (MDR) with a high Q factor is used as a sensor which is interrogated by incorporating the MDR into a microwave photonic filter (MPF) consisting of a laser source, a phase modulator (PM), the MDR, and a photodetector (PD), with the central frequency of the MPF being a function of the resonant wavelength of the MDR. A broadband linearly chirped microwave waveform (LCMW) is applied to the input of the MPF to generate a filtered microwave waveform. By measuring the temporal location of the filtered microwave waveform, the sensing information is revealed. To increase the signal-to-noise ratio (SNR) of the filtered microwave waveform, a phase-only filter (POF) realized based on the LCMW is correlated with the filtered microwave waveform, to generate a compressed pulse, which is filtered using a Hamming window to remove the noise and recorrelated with the POF to recover the filtered microwave waveform. Since the SNR is significantly increased, the interrogation accuracy is improved. The use of the proposed sensor for temperature and refractive index (RI) sensing is performed. The experimental results show that the sensor has a sensitivity of 76.8 pm/°C and a resolution of 0.234 °C as a temperature sensor, and a sensitivity of 33.28 nm/RIU and a resolution of 1.32 × 10-3RIU as an RI sensor. The interrogation speed is as high as 100 kHz.
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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 | 0.000 | 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".