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Record W3005481134 · doi:10.1109/jmems.2020.2968069

A Piezo-Avalanche Accelerometer

2020· article· en· W3005481134 on OpenAlexafffund
Abbin Perunnilathil Joy, M. A. Kanygin, Behraad Bahreyni

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAccelerometerStress (linguistics)VoltageAvalanche diodeBeam (structure)Breakdown voltageOptoelectronicsSensitivity (control systems)Spring (device)Air gap (plumbing)AcousticsOpticsElectrical engineeringStructural engineeringPhysicsComposite materialElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

We are reporting on the first use of the piezo-avalanche effect in a micromachined sensor for the measurement of inertial forces. The device integrates a pn junction at the base of a beam that is used as the spring for the microstructure. Movements of a proof-mass at the opposite end of the beam produce stress at the location of the junction. To measure this stress, the junction is forced into its reverse breakdown region while monitoring its current-voltage relationship. The mechanical stress modifies the bandgap of the material, eventually leading to a change in its breakdown voltage. It is shown that this simple structure provides a high sensitivity of ~3 nA/g when the device is biased with a fixed voltage in its breakdown region. The device operation is modeled analytically, and results are found to be in good agreement with the experiments. The piezo-avalanche effect scales favorably and can be utilized for stress measurements at arbitrary locations on a structure as well as at nanoscales.

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)0.000
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.187
Teacher spread0.171 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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