Sulfur-rich chalcogenide claddings for athermal and high-Q silicon microring resonators
Bibliographic record
Abstract
Heterogeneous integration of materials with a negative thermo-optic coefficient is a simple and efficient way to compensate the strong detrimental thermal dependence of silicon-on-insulator devices. Yet, the list of materials that are both amenable for photonics fabrication and exhibit a negative TOC is very short and often requires sacrificing loss performance. In this work, we demonstrate that As20S80 chalcogenide glass thin-films can be used to compensate silicon thermal effects in microring resonators while retaining excellent loss figures. We present an experimental characterization of the glass thin-film and of fabricated hybrid microring resonators at telecommunication wavelengths. Nearly athermal operation is demonstrated for the TM polarization with an absolute minimum measured resonance shift of 5.25 pm K−1, corresponding to a waveguide effective index thermal dependence of 4.28×10-6 RIU/K. We show that the thermal dependence can be controlled by changing the cladding thickness and a negative thermal dependence is obtained for the TM polarization. All configurations exhibit unprecedented low loss figures with a maximum measured intrinsic quality factor exceeding 3.9 × 105, corresponding to waveguide propagation loss of 1.37 dB cm−1. A value of−4.75(75)×10-5 RIU/K is measured for the thermo-optic coefficient of As20S80 thin-films.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".