Optimal ultra-miniature polarimeters in silicon photonic integrated circuits
Bibliographic record
Abstract
Measurement of the state of polarization of light is essential in a vast number of applications, such as quantum and classical communications, remote sensing, astronomy, and biomedical diagnostics. Nanophotonic structures and integrated photonic circuits can, in many circumstances, replace conventional discrete optical components for miniature polarimeters and chip-scale polarimetry systems and thus significantly improve robustness while minimizing the footprint and cost. We propose and experimentally demonstrate two silicon photonic four-photodetector (PD) division-of-amplitude polarimeters (4PD-DOAPs) using a complementary metal–oxide–semiconductor-compatible photonic fabrication process. The first design targets minimizing the number of optical components. The second design makes use of a slightly more complex circuit design to achieve an optimal frame for measurements; this measurement frame minimizes and equalizes estimation variances in the presence of the additive white Gaussian noise and the signal dependent shot noise. Further theoretical examination reveals that within the optimal measurement frames for Stokes polarimeters, the DOAP with four PDs has the minimal equally weighted variance compared to those with a greater number of PDs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".