Controlling thermal-induced dewetting of As<sub>20</sub>Se<sub>80</sub> thin films for integrated photonics applications
Bibliographic record
Abstract
As the use of photonics circuits expands, the optical quality and performance of integrated components in the microscale become a major concern. Aiming to improve the performance while reducing the time processing, new microfabrication approaches are being investigated. The dewetting of glassy thin films have been recently proposed as an alternative for nano and microfabrication of chalcogenide optical components. Besides being the best materials for light transmission in the infrared region, chalcogenide glasses possess a flexible molecular structure that allows using a cheap and simple molding process. Here we investigate the thermal-induced dewetting of chalcogenide As20Se80 thin films, by studying the influence of temperature, atmosphere, and heating rate on the formation of self-assembled microstructures. We found that thin films between 150 and 700 nm dewet via structural relaxation, similarly to liquid agglomeration, and produce solid microstructures with the same composition and molecular structure as the initial film. By controlling the glass viscosity and the kinetics of the nucleation process it was possible to adjust the distribution and size of glassy microstructures. Additionally, we combine the dewetting process with standard photolithography and by avoiding the capillary instabilities, we are capable to obtain waveguides with the smooth and symmetric surfaces required for optical applications in the microscale size.
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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".