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Record W4403036354 · doi:10.1116/6.0003976

Low-loss and low-temperature Al2O3 thin films for integrated photonics and optical coatings

2024· article· en· W4403036354 on OpenAlexafffund
Pooya Torab Ahmadi, Michaël Chesaux, Jacek Wójcik, Dino Deligiannis, Peter Mascher, Jonathan D. B. Bradley

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2024
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcMaster University
FundersMitacsOntario Research Foundation
KeywordsPhotonicsMaterials scienceThin filmOptoelectronicsEngineering physicsOpticsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Amorphous aluminum oxide (Al2O3) is a key material in optical coatings due to its notable properties, including a broad transparency window (ultraviolet to mid-infrared) and excellent durability. Moreover, its higher refractive index contrast relative to silica cladding layers and high solubility of rare-earth ions make it well suited for optical waveguides and the development of various functionalities in integrated photonics. In many coatings and integrated photonics applications, the substrates are temperature and stress sensitive, while relatively thick (∼1 μm) alumina layers are required; thus, it is crucial to fabricate low optical loss alumina thin films at low deposition temperatures, while maintaining high deposition rates. In this study, plasma-assisted reactive magnetron sputtering, operated in an alternating current mode, is investigated as a reliable, straightforward, and wafer-scale compatible technique for the deposition of high optical quality and uniform Al2O3 thin films at low temperature. One-micrometer-thick amorphous Al2O3 planar waveguides, deposited at 150 °C and a rate of 23.3 nm/min, exhibit optical losses below 1 dB/cm at 638 nm and as low as 0.1 dB/cm in the conventional optical communication band.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.079
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.234
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2024
Admission routes2
Has abstractyes

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