Phase errors and statistical analysis of silicon-nitride arrayed waveguide gratings
Bibliographic record
Abstract
We present a statistical analysis of arrayed waveguide gratings (AWGs) in the presence of phase errors in the optical waveguides caused by fabrication process variations. Important figures of merit, such as the insertion loss, crosstalk, and non-uniformity, are parameterized as a function of the coherence length, a physical parameter that characterizes the accumulated phase errors in optical waveguides and that can be extracted by measuring variations in the resonant wavelengths of Mach-Zehnder interferometers. A die-level coherence length of 23.7 mm is measured for sub-micrometer-thick silicon nitride (SiN) waveguides fabricated using a 200-mm wafer process. Through Monte Carlo simulations using a semi-analytical model, we examine the impacts of phase errors on the performance of AWGs with 200 GHz and 100 GHz channel spacings. Our results show that the waveguide phase errors cause remarkable excess insertion loss and crosstalk in an AWG, and also increase non-uniformity across channels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".