Characterization and compensation of apodization phase noise in silicon integrated Bragg gratings
Bibliographic record
Abstract
Precise and reliable apodization of silicon integrated Bragg gratings (IBGs) is the key to realizing their spectral tailoring for many optical applications such as optical signal processing and wavelength-division multiplexing systems. However, apodization in a silicon IBG that is typically realized by modifying the physical waveguide grating structure can also introduce unwanted grating phase variations that can affect the grating response. In this paper, we present a model to characterize apodized silicon IBGs which can take such apodization phase noise (APN) into account, based on direct synthesis of the physical grating structure. The model is used to characterize a set of different silicon IBGs apodized by lateral misalignment (ΔL) and duty-cycle (DC) modulations and designed with different responses, and the results show that the APN can greatly distort the complex responses of the gratings. Then, we develop a methodology to compensate the APN and thus to correct the distorted grating responses. The designed silicon IBGs were fabricated and tested experimentally. The accuracy of the model is examined by comparing the measured grating spectra with those predicted by the model. Spectral corrections are then demonstrated in Gaussian-apodized gratings based on ΔL- and DC-modulated silicon IBGs and a square-shaped filter developed on a ΔL-modulated IBG. Finally, a complex spectral correction of a photonic Hilbert transformer developed on a ΔL-modulated silicon IBG is achieved.
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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".