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Record W4255625690 · doi:10.32920/ryerson.14643810

Measurement of the properties of MetalMUMPS® thin films

2021· preprint· en· W4255625690 on OpenAlexaff
John Shih-Hua Chang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceSilicon nitrideResidual stressSiliconNitrideMicroelectromechanical systemsComposite materialCantileverThin filmNickelModulusElastic modulusStress (linguistics)OptoelectronicsMetallurgyNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

The metal multi-user micro-electro-mechanical-systems (MEMS) processes (MetalMUMPs®) micro-machining process includes two silicon nitride films, one polysilicon film, and one nickel film for constructing various MEMS devices. This thesis presents property measurements of the metalMUMPs® silicon nitride and nickel films. Fabricated MetalMUMPs® silicon nutride prototypes were used to experimentally determine a Young’s modulus of 209 GPa and a residual stress difference of 169 MPa for the silicon nitride films. A method, which uses the deformations along the width of bi-layered cantilever beams, was proposed to determine the residual stress difference of the two silicon nitride films. Fabricated MetalMUMPs® nickel prototypes were used to experimentally extract a Young’s modulus of 159 GPa and a residual stress gradient of -4.72 MPa/m for the nickel film. A micro bridge mechanism was developed to lift long silicon nitride beams for the determination of the residual stress difference of the two silicon nitride films.

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 score0.005

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 head0.042
GPT teacher head0.210
Teacher spread0.167 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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