Etchless chalcogenide microresonators monolithically coupled to silicon photonic waveguides
Bibliographic record
Abstract
Integration of chalcogenide waveguides in silicon photonics can mitigate the prohibitive nonlinear losses of silicon while leveraging the mature complementary metal–oxide–semiconductor (CMOS)-compatible nanophotonic fabrication process. In this work, we demonstrate, for the first time, to the best of our knowledge, a method of integrating high- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> chalcogenides microring resonators onto the silicon photonics platform without post-process etching. The method uses micro-trench filling and a novel thermal dewetting technique to form low-loss chalcogenide strip waveguides. The microrings are integrated directly inside silicon photonic circuits through evanescent coupling, providing an uncomplicated hybrid integration scheme without the need to modify the existing photonics foundry process. The microrings show a high quality factor exceeding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>6</mml:mn> <mml:mo>×</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> </mml:mrow> </mml:math> near 1550 nm and propagation losses below 0.7 dB/cm, indicating a promising solution for low-cost, compact nonlinear photonic devices with applications in various fields such as telecommunications and spectroscopy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".