Amplification in a Hybrid Erbium Silicon Photonic Waveguide
Bibliographic record
Abstract
The monolithic integration of optical amplifiers and light sources on silicon-on-insulator (SOI) remains a significant challenge despite the widespread application of silicon photonic integrated circuits [1]. Current approaches for integrating active gain media onto silicon chips include the heterogeneous integration of III - V materials [2], parametric gain in materials such as gallium phosphide [3], and planar waveguide amplifiers based on erbium-doped optical thin films such as Al2O3, LNOI, and Si3N4[4]–[6]. Hybrid platforms that aim for monolithic integration of erbium-based amplifiers are particularly appealing due to their low cost, relatively simple fabrication processes, scalability, and compatibility with various standard foundry processes. These platforms have been successfully shown on Si3N4[7], [8], however, extending these approaches to the SOI platform is more challenging due to higher silicon waveguide losses, lower confinement of light in the gain medium, and two-photon absorption in silicon.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".