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Record W2579479237 · doi:10.1109/icsens.2016.7808650

Design, fabrication and characterization of a high performance MEMS accelerometer

2016· article· en· W2579479237 on OpenAlexaff
Fatemeh Edalatfar, Bahareh Yaghootkar, Abdul Qader Ahsan Qureshi, S. Azimi, Behraad Bahreyni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAccelerometerDeep reactive-ion etchingMicroelectromechanical systemsMaterials scienceCapacitive sensingGyroscopeFabricationSensitivity (control systems)Bandwidth (computing)Electronic engineeringSilicon on insulatorWaferOptoelectronicsComputer scienceElectrical engineeringEtching (microfabrication)Reactive-ion etchingEngineeringNanotechnologySiliconAerospace engineering

Abstract

fetched live from OpenAlex

High performance accelerometers have wide variety of applications in inertial navigation, seismic imaging, and conditional health monitoring. This paper reports mechanical design, fabrication, and characterization of an in-plane MEMS accelerometer. The sensor has been designed in such a way to meet both the high sensitivity and the wide bandwidth requirements in one single device. The MEMS sensor, which is an interdigitated capacitive accelerometer, is fabricated from a 100 um thick SOI wafer using deep reactive ion etching (DRIE) process. Theoretical results were confirmed by experimental measurements. Sensor has a resonance frequency at 2.3 KHz. Voltage sensitivity of the device is 11.8mV/g. The mechanical-thermal noise of the device is 193ng/rtHz. These characteristics make the device a promising candidate for applications where both the high sensitivity and the wide bandwidth are required.

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 score0.247
Threshold uncertainty score0.110

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 head0.197
Teacher spread0.181 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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