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Record W2036762808 · doi:10.1088/0960-1317/21/4/045001

Measurement of MEMS thermal actuator time constant using image blur

2011· article· en· W2036762808 on OpenAlexaff
Ben Bschaden, Ted Hubbard, Marek Kujath

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsActuatorAmplitudeTime constantConstant (computer programming)ThermalMicroelectromechanical systemsOpticsAcousticsMotion blurThermal conductionMaterials scienceControl theory (sociology)MechanicsPhysicsEngineeringComputer scienceImage (mathematics)Artificial intelligenceElectrical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

A method of experimentally determining the time constants of MEMS thermal actuators from an analysis of blurred still camera images of high frequency motion is presented. The response of MEMS thermal actuators to harmonic excitation falls off with increasing frequency. An analytical derivation of the thermal frequency response is performed considering actuator arm conduction and conduction through air to the substrate. For the actuator in this study, the predicted time constant is 120–125 µs. Directly measuring this time constant would require sampling in the kHz range, well above the range of most cameras, which blur the image. The thermal time constant is computed by analyzing the sidewall profiles of a stationary actuator and of the actuator excited over a range of frequencies. The observed slope of the blurred sidewall profile changes with the frequency: the larger the amplitude of motion, the shallower the apparent slope. By numerically blurring the stationary profile, the relationship between the blur amplitude and the blurred slope is found. From this and the measured slopes, the blur amplitude is determined at each frequency and the time constant determined by curve fitting. The resultant measured time constant is 130 ± 12 µs, in good agreement with the predicted value.

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 score0.015
Threshold uncertainty score0.627

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 head0.017
GPT teacher head0.188
Teacher spread0.172 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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