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Record W2528586090 · doi:10.1109/ted.2016.2612586

Viability of Piezojunction Effect for Microresonator Applications

2016· article· en· W2528586090 on OpenAlexafffund
Amin Rasouli, Behraad Bahreyni

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityCMC Microsystems
KeywordsCapacitive sensingResonatorDiodeBiasingOptoelectronicsMaterials sciencePower (physics)SIGNAL (programming language)Q factorCMOSElectrical engineeringElectronic engineeringAcousticsPhysicsEngineeringVoltageComputer science

Abstract

fetched live from OpenAlex

This paper presents a study on the application of piezojunction transduction for the detection of vibrations of resonant microdevices. The piezojunction effect refers to the dependence of the electrical characteristics of a p-n junction to mechanical stress. It is shown that the piezojunction signal is proportional to the bias current of the diode, which can be adjusted as needed. A simple model that accounts for both capacitive and piezojunction currents and the equivalent electrical representations of the microdevice are developed and verified. A bulk-mode extensional resonator with an integrated p-n junction was designed and fabricated to serve as a proof-of-concept device. The static and dynamic responses of the fabricated devices were measured and compared against the models. The extensional-mode frequency of the resonator was measured to be ~7 MHz with a mechanical quality factor of ~1400. Capacitive and piezojunction signals at the output of the device were isolated and studied. It is shown that even with diode bias currents on the order of a few microamperes, the piezojunction and capacitive currents are comparable. Experimental verification demonstrates that piezojunction effect is a promising addition to the existing detection techniques in the resonance-based applications, where small chip area, integration, and power consumption are key requirements.

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: none
Teacher disagreement score0.679
Threshold uncertainty score0.361

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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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