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Record W2945032985 · doi:10.1063/1.5093553

Measurement of mechanical strain based on piezo-avalanche effect

2019· article· en· W2945032985 on OpenAlexafffund
Abbin Perunnilathil Joy, M. A. Kanygin, Behraad Bahreyni

Bibliographic record

VenueApplied Physics Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPiezoresistive effectMiniaturizationMaterials scienceStress (linguistics)OptoelectronicsVoltageSensitivity (control systems)Electrical engineeringNanotechnologyElectronic engineering

Abstract

fetched live from OpenAlex

We report on the use of the breakdown voltage of a pn junction to measure mechanical strain in microstructures. The working principle relies on the dependence of the silicon bandgap on the mechanical stress which affects the current-voltage characteristics of the pn junction. An analytical model is developed and verified experimentally for the phenomenon. A micromechanical device with integrated junctions was designed and fabricated. Mechanical stress was applied onto the structure by subjecting it to mechanical vibrations. It is shown that the breakdown voltage of the device exhibited a high stress sensitivity of about 240 μV/MPa. The mechanical stress can also be measured by monitoring the device current while biased at a constant voltage. In this mode, the steep changes of the junction current in the breakdown region led to nearly a tenfold higher stress sensitivity compared to a piezoresistive sensor. The high sensitivity, simple measurement, and potential for miniaturization for piezo-avalanche sensing make it a promising technique for the measurement of stress in micro- and nanomechanical devices.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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