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Record W2134288793 · doi:10.2528/pier06081404

TWO NOVEL STRUCTURES FOR TUNABLE MEMS CAPACITOR WITH RF APPLICATIONS

2007· article· en· W2134288793 on OpenAlexfundno aff
Ebrahim Abbaspour-Sani, N. Nasirzadeh, Gholamreza Dadashzadeh

Bibliographic record

VenueElectromagnetic waves · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsnot available
FundersIran Telecommunication Research CenterUniversity of Waterloo
KeywordsMicroelectromechanical systemsCapacitorMaterials scienceOptoelectronicsElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

Two novel structures for high-Q MEMS tumble capacitors are presented.The proposed designs include full plate as well as the comb structured capacitors.They can be fabricated employing surface micromachining technology which is CMOS-compatible.The structures do not require the cantilever beams which introduce considerable series resistance to the capacitor and decrease the quality factor.Therefore, our proposed structures achieve better Q in a smaller die area.The simulated results for 1 pF full plate capacitor shows a tuning range of 42% and a Q of 47 at 1 GHz.However, with the same initial capacitance, but the comb structure, the tuning range is increased to 43% but the Q is decreased to 45 at 1 GHz.The simulated Pull-in voltage with no residual stress is 3.5 V for both capacitors.The S 11 responses are reported for a frequency range from 1 up to 4 GHz.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.225
Teacher spread0.219 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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