MétaCan
Menu
Back to cohort
Record W2765145920 · doi:10.1109/fcs.2017.8088846

Capacitive Lamé mode resonator with gap closing mechanism for motional resistance reduction

2017· article· en· W2765145920 on OpenAlexaff
Mohannad Y. Elsayed, Frédéric Nabki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsResonatorMaterials scienceCapacitive sensingOptoelectronicsFabricationQ factorElectrodeOscillation (cell signaling)Closing (real estate)SiliconResonance (particle physics)VoltageElectrical engineeringPhysicsEngineeringChemistryAtomic physics

Abstract

fetched live from OpenAlex

This work presents a Lamé mode resonator featuring a novel gap closing mechanism which employs electrostatic force to reduce the capacitive transduction gaps to sub-micron values to overcome fabrication technology limitations. This leads to significant resonator loss and motional resistance reduction, which is highly advantageous as it simplifies the design of oscillation sustaining circuitry. Prototypes were fabricated in a commercial silicon-on-insulator technology, PiezoMUMPs from MEMSCAP. Upon the application of a DC voltage of 55 V between the resonator structure and electrodes, the gaps are reduced from 2.5 μm to 0.5 μm. A resonance frequency of 18 MHz with a quality factor of 120,000 was observed under 1 mTorr vacuum. A loss of 55 dB was measured at 55 V, which corresponds to a motional resistance of 56 kΩ, 5 times lower than that of a similar design without gap closers at the same voltage. The frequency tuning range is also increased significantly as a result of the gap reduction, which can be very useful for overcoming ambient conditions and fabrication variations.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.258
Teacher spread0.235 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207