On-Chip Sensor for Simultaneous Temperature and Refractive Index Measurements Based on a Dual-Passband Microwave Photonic Filter
Bibliographic record
Abstract
We propose and experimentally demonstrate an on-chip optical sensor based on a dual-passband microwave photonic filter (MPF) incorporating a silicon photonic integrated microdisk resonator (MDR). Two whispering gallery modes supported by the MDR that are experiencing different wavelength shifts are employed for simultaneous temperature and refractive index (RI) measurements. To increase the interrogation speed and resolution, the MDR is incorporated in an MPF to produce two microwave passbands. By applying a broadband linearly chirped microwave waveform to the MPF, two filtered microwave waveforms with their temporal locations or equivalently central frequencies corresponding to the wavelength shifts of the notches are generated. The measurement of the temporal locations or equivalently the central frequencies is performed at high speed and high resolution using a digital signal processor. To increase the signal-to-noise ratio of the filtered microwave waveforms, a noise reduction algorithm based on phase-only filtering is proposed and employed. The on-chip optical sensing system is experimentally demonstrated. The sensing system can provide high temperature and RI interrogation resolutions of 2.4 × 10-5°C and 9.1 × 10-8RIU at a high interrogation speed of 1 MHz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".