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Record W2159237362 · doi:10.1109/tdei.2012.6259990

Dielectric layers for RF-MEMS switches: Design and study of appropriate structures preventing electrostatic charging

2012· article· en· W2159237362 on OpenAlexfundno aff
Kremena Makasheva, Bernard Despax, Laurent Boudou, G. Teyssèdre

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsnot available
FundersUniversité de Montréal
KeywordsDielectricMicroelectromechanical systemsMaterials scienceLayer (electronics)Reliability (semiconductor)OptoelectronicsCharacterization (materials science)Electrical conductorPlasmaElectronic engineeringNanotechnologyEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

In this work we present results from an experimental study on the physico-chemical and electrical properties of thin dielectric layers (SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> ) deposited using a plasma assisted process. An application of those dielectric layers in RF-MEMS switches is aimed at. We look for a better understanding of dielectric charging phenomenon as it is known to be the main cause for the failure and low reliability in the actuation of RFMEMS switches. In order to improve the performance of dielectric layers used in RFMEMS we have tested a new concept: instead of elaborating new materials we assembled a dielectric multi-layer structure that gains from design rather than from composition. To achieve this we have started with a characterization of the mono-layers deposited singly. The multi-layer structure was found to be a promising candidate for suitable modulation of the conductive properties of the deposited dielectric layers for their use in RF-MEMS switches.

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 score0.463
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

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