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Record W2908527898 · doi:10.1063/1.5053597

Dimpled electrostatic MEMS actuators

2019· article· en· W2908527898 on OpenAlexafffund
Ayman M. Alneamy, Majed Al‐Ghamdi, Sangtak Park, Mahmoud Khater, Eihab Abdel‐Rahman, G. R. Heppler

Bibliographic record

VenueJournal of Applied Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
FundersKing Fahd University of Petroleum and MineralsKing Abdulaziz City for Science and TechnologyJazan UniversityCMC Microsystems
KeywordsActuatorStictionDimpleIntermittencyBifurcation diagramChaoticMaterials scienceCascadeMechanicsBifurcationControl theory (sociology)InstabilityAmplitudeMicroelectromechanical systemsPhysicsOptoelectronicsComputer scienceOpticsEngineeringNonlinear systemElectrical engineering

Abstract

fetched live from OpenAlex

We present electrostatic Micro-Electromechanical System actuators equipped with dimples and contact pads. The introduction of dimples and contact pads is shown to prevent stiction between the actuator and bottom electrodes and minimize dielectric charging and to eliminate multi-valuedness in the response. It also increases the stable travel range by eliminating the pull-in instability and introducing new “tapping mode” oscillations, where the dimples come into regular contact with the contact pads. An experimentally validated lumped-mass model was developed and used to generate the bifurcation diagram of the actuator in the vicinity of tapping mode oscillations. The diagram showed the presence of a chaotic attractor bracketed by a type-I intermittency and a cascade of period-doubling bifurcations. However, these chaotic motions were only present for a limited range of the excitation amplitude and frequency. Provided these ranges are excluded, dimples and contact pads can be deployed to obtain efficient and well regulated electrostatic actuators. We found that these results and conclusions are valid for classes of low-frequency as well as high-frequency actuators.

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 score0.084
Threshold uncertainty score0.341

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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