Automated control algorithms for silicon photonic polarization receiver
Bibliographic record
Abstract
We demonstrate greedy linear descent-based, basic gradient descent-based, two-point step size gradient descent-based, and two-stage optimization method-based automated control algorithms and examine their performance for use with a silicon photonic polarization receiver. With an active feedback loop control process, time-varying arbitrary polarization states from an optical fiber can be automatically adapted and stabilized to the transverse-electric (TE) mode of a single-mode silicon waveguide. Using the proposed control algorithms, we successfully realize automated adaptations for a 10 Gb/s on-off keying signal in the polarization receiver. Based on the large-signal measurement results, the control algorithms are examined and compared with regard to the iteration number and the output response. In addition, we implemented a long-duration experiment to track, adapt, and stabilize arbitrary input polarization states using the two-point step size gradient descent-based and two-stage optimization method-based control algorithms. The experimental results show that these control algorithms enable the polarization receiver to achieve real-time and continuous polarization management.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".