(Invited) on-Chip and Silicon-Compatible Er-Doped Aluminum Oxide Lasers
Bibliographic record
Abstract
Erbium-doped lasers provide high power, narrow linewidth and thermally stable emission in the important 1.5-µm eye-safe and telecommunications band. By integrating such lasers on a chip we can realize cost-effective, compact and highly robust devices compared to fiber-based platforms. Furthermore, using a silicon-compatible fabrication approach allows for co-integration with silicon electronic/photonic devices and will open new applications for compact microsystems. The presentation will cover our work on silicon-based erbium-doped aluminum oxide lasers (Al2O3:Er3+). Al2O3:Er3+ has recently received significant attention because of its broad emission, reduced clustering, and higher index, thus potential for more compact devices, compared to Er-doped silica. This has led to numerous demonstrations of amplification and lasing on chips using Al2O3:Er3+ as monolithic gain medium. As an important step towards silicon compatibility and exploiting wafer-scale lithography methods for high resolution cavity features, we have developed a silicon nitride-based Al2O3:Er3+ platform. Using such an approach, we have demonstrated a number of on-chip lasers, including distributed feedback, distributed Bragg reflector and microcavity devices. The talk will cover critical materials and design considerations towards realizing high performance lasers, including the influence of ion-ion clustering in the Al2O3 host, nanoscale film-thickness non-uniformities, and cavity Q factor optimization.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".