Four-Arm Grating Couplers for Wavefront Sensing Applications
Bibliographic record
Abstract
Atmospheric turbulence in free space optical satellite downlinks negatively impacts link availability and bit error rate. These effects can be mitigated using a compensation system capable of measuring the incoming wavefront distortion and applying a suitable correction to the received signal. The traditional solution based on adaptive optics and the Shack-Hartmann wavefront sensor has limitations in bandwidth, system complexity, size, weight, and power consumption. Signal correction can also be accomplished using a novel single-chip silicon photonic solution. This work introduces a four-arm grating coupler structure acting as a wavefront sensing element that emulates the performance of the Shack-Hartmann wavefront sensor by giving local tip and tilt estimation. FDTD simulations and measurements have confirmed the presence of a monotonic relationship between incident angle, polarization, and coupler output which can be converted to phase estimation through a reconstruction algorithm. An array of four-arm couplers on a silicon photonic chip provides enough sampling to fully reconstruct the wavefront, providing benefits over traditional solutions such as higher bandwidth, reduced size and weight, and reduced cost. Scaling up the results of this work to a full device could provide a solution for free space optical satellite to ground links in remote and rural communities across Canada and around the world.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".