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Record W1155069466 · doi:10.1116/1.4923029

Observation of ultraslow stress release in silicon nitride films on CaF2

2015· article· en· W1155069466 on OpenAlexafffund
Tianyi Guo, M. Jamal Deen, Chang‐Qing Xu, Qiyin Fang, P. Ravi Selvaganapathy, Haiying Zhang

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2015
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceSubstrate (aquarium)Silicon nitrideStress (linguistics)SiliconChemical vapor depositionX-ray photoelectron spectroscopyNitrideThin filmComposite materialAtmosphere (unit)Deposition (geology)NanotechnologyOptoelectronicsLayer (electronics)Chemical engineering

Abstract

fetched live from OpenAlex

Silicon nitride thin films are deposited by plasma-enhanced chemical vapor deposition on (100) and (111) CaF2 crystalline substrates. Delaminated wavy buckles formed during the release of internal compressive stress in the films and the stress releasing processes are observed macroscopically and microscopically. The stress release patterns start from the substrate edges and propagate to the center along defined directions aligned with the crystallographic orientations of the substrate. The stress releasing velocity of SiNx film on (111) CaF2 is larger than that of SiNx film with the same thickness on (100) CaF2. The velocities of SiNx film on both (100) and (111) CaF2 increase with the film thickness. The stress releasing process is initiated when the films are exposed to atmosphere, but it is not a chemical change from x-ray photoelectron 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.233
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2015
Admission routes2
Has abstractyes

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