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Record W3161242610 · doi:10.1364/ome.423938

Controlling thermal-induced dewetting of As<sub>20</sub>Se<sub>80</sub> thin films for integrated photonics applications

2021· article· en· W3161242610 on OpenAlexafffund
Y.N. Colmenares, Wagner Correr, Sandra Helena Messaddeq, Younès Messaddeq

Bibliographic record

VenueOptical Materials Express · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsDewettingMaterials scienceMicrofabricationMicroscale chemistryChalcogenideThin filmChalcogenide glassNanotechnologyMicrostructureMicrolensPhotonicsOptoelectronicsOpticsComposite materialFabricationLens (geology)

Abstract

fetched live from OpenAlex

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 As 20 Se 80 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.225
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes2
Has abstractyes

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