Templated dewetting for self-assembled ultra-low-loss chalcogenide integrated photonics
Bibliographic record
Abstract
Integrated photonics is of growing interest but relies on complex fabrication methods that have yet to match optical losses of bulkier platforms like optical fibers or whispering gallery mode resonators. Spontaneous matter reorganization phenomenon (e.g. dewetting) in thin-films provides a way for self-assembled structures with atomic scale surface rugosity, potentially alleviating the problems of roughness scattering loss and fabrication complexity. In this article, we study solid-state dewetting in chalcogenide glass thin-films and demonstrate its applicability to the fabrication of high-quality integrated photonics components. Optimal dewetting parameters are derived from a comprehensive experimental study of thin-film properties under high temperature rapid annealing. Atomic scale surface roughness are obtained using dewetting, with RMS values as low as R q = 0.189 nm. Several integrated photonics components are fabricated using the method and characterized. We show that the use of pre-patterned templates leads to organized, reproducible patterns with large-scale uniformity and demonstrate the record high quality-factor of 4.7 × 106 in compact (R = 50 µm) microdisks, corresponding to 0.08 dB⋅cm−1 waveguide propagation loss. The integrated devices are directly fabricated on standard silicon-on-insulator dice using the micro-trench filling technique and coupled to silicon waveguides, making them readily deployable with existing silicon devices and systems.
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 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) | 0.000 | 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 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".