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Record W2888097411 · doi:10.1063/1.5045516

Investigation for the structural stress of SiO2 thin films and its distribution on the large-wafer created by plasma enhanced chemical vapor deposition

2018· article· en· W2888097411 on OpenAlexaff
DeGui Sun, Qingyu Sun, Wenchao Xing, Zheyu Sun, Hongpeng Shang, Liyuan Chang, Xue-ping Wang, Peng Liu, Trevor J. Hall

Bibliographic record

VenueAIP Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Ottawa
FundersChangchun University of Science and TechnologyDepartment of Science and Technology of Jilin Province
KeywordsMaterials scienceResidual stressPlasma-enhanced chemical vapor depositionWaferStress (linguistics)Chemical vapor depositionSubstrate (aquarium)Composite materialThin filmSiliconCompressive strengthUltimate tensile strengthDeposition (geology)NanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

For a multilayered configuration of SiO2 film created by plasma enhanced chemical vapor deposition (PECVD), the thermal stress and growth-caused stress are two intrinsic stresses. In this work, based on the interactions of all the layers of film, a nonlinearly distributed structural stress over a large substrate is found. The numerical simulations for the nonlinear distribution of the structural stress and the uniform distributions of the two intrinsic stresses are carried out. As a result, the tensile structural stress decreases by ∼4x105MPa from center to edge of a 6” silicon wafer and the compressive growth-caused stress increases by ∼5x105MPa corresponding to the growth-rate increase of 40nm/s, which matches a ∼120MPa distribution of residual compressive stress obtained with in-situ measurements of film samples. In simulations, it is also discovered that the initial curvature of substrate has an impressive influence on the later grown film.

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 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 score0.052
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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