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Record W1510478941 · doi:10.1109/iscas.2015.7168713

Test method for capacitive MEMS devices utilizing pierce oscillator

2015· article· en· W1510478941 on OpenAlexafffund
Ali Dianat, Ali Attaran, Rashid Rashidzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Windsor
FundersCMC Microsystems
KeywordsMicroelectromechanical systemsCapacitive sensingResonatorComb driveFrequency domainElectronic engineeringTime domainMaterials scienceComputer scienceAcousticsElectrical engineeringEngineeringOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

In this paper a test method for MEMS devices is presented in which physical defects are detected in the frequency domain rather than the time domain. A resonator, that can be part of a read out circuit, is utilized to test capacitive Micro-Electro-Mechanical Systems (MEMS). The proposed technique is based on the principle of resonant frequency where variations of the resonant frequency are observed to detect structural defects. To verify the validity of the proposed approach, a MEMS comb-drive is designed and fabricated. Measurement and simulation results indicate that the proposed method can be used to capture common comb-drive defects such as missing or broken fingers, shorted fingers and tilted arms.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.380
Threshold uncertainty score0.439

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 head0.048
GPT teacher head0.304
Teacher spread0.256 · 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
GenreMethods

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

Citations5
Published2015
Admission routes2
Has abstractyes

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