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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".