(Invited) Rare Earth Doped Light Emitting Thin Film Materials for Silicon Photonics
Bibliographic record
Abstract
Silicon photonic technology is becoming ubiquitous for data center, sensing and advanced photonic applications. However, one of the key challenges for silicon photonic microsystems is integrating materials which can provide optical gain. The leading approach involves hybrid bonding of III-V materials to silicon chips, which is expensive and difficult to scale. Alternatively, rare-earth-doped materials are promising for active device applications on silicon. Rare earth doped oxide thin films can be deposited using standard, low-cost, and wafer-scale methods directly on silicon and emit light in important bands for communications and other emerging applications. This presentation will cover recent progress on integrating rare-earth-doped materials into silicon photonic microsystems. It will focus on fabrication and integration methods, particularly reactive magnetron co-sputtering, which yields low-loss, high-gain thin films which can be deposited using a single post-processing step onto silicon photonic chips that have been fabricated in a silicon foundry. The fabrication challenges, spectroscopic properties, and device design requirements related to prospective materials, including ytterbium-, erbium- and thulium-doped oxides will be discussed. The talk will review the application of such films in light-emitting rare-earth-doped devices, including amplifiers and continuous-wave and pulsed lasers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.023 |
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".