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Record W2518965295 · doi:10.1088/0960-1317/25/9/095004

Nano-scale measurement of sub-micrometer MEMS in-plane dynamics using synchronized illumination

2015· article· en· W2518965295 on OpenAlexafffund
Stephan Warnat, Cameron Forbrigger, Marek Kujath, Ted Hubbard

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsMicroelectromechanical systemsMicrometerNano-Nanoscopic scaleScale (ratio)NanotechnologyMaterials scienceDynamics (music)OpticsOptoelectronicsPhysicsAcousticsComposite material

Abstract

fetched live from OpenAlex

A method for measuring the sub-micrometer in-plane dynamics of MEMS devices with nano-scale precision using a CCD camera and synchronized pulsating illumination is presented. Typical MEMS actuators have fast responses (generally in the 1–200 kHz range), much faster than typical cameras which record a time averaged motion. Under constant illumination the average displacement is steady state and independent of dynamic amplitude or phase. Methods such as strobe illumination use short light pulses to freeze the motion. This paper develops the use of longer pulses of illumination that do not freeze the image, but make the average displacement depend on dynamic amplitude and phase; thus allowing both properties to be extracted. The expected signal is derived as a function of light pulse width and delay, and short versus longer pulses are compared. Measurements using a conventional microscope with replacement of the lamp with LEDs confirmed the derived equations. The system was used to measure sub-micrometer motion of MEMS actuators with ~5 nm precision. The time constant of a thermal actuator was measured and found to be 48 µ s. A resonant peak of a MEMS device was measured at 123.30 kHz with an amplitude of 238 nm.

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 score0.044
Threshold uncertainty score0.718

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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