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Record W2980693647 · doi:10.1109/jsen.2019.2948129

Development of Doped Silicon Multi-Element Stress Sensor Rosette With Temperature Compensation

2019· article· en· W2980693647 on OpenAlexafffund
Mohammed O. Kayed, Amr A. Balbola, Edmond Lou, Walied A. Moussa

Bibliographic record

VenueIEEE Sensors Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPiezoresistive effectMaterials scienceStress (linguistics)SiliconCompensation (psychology)BendingRosette (schizont appearance)Composite materialOptoelectronics

Abstract

fetched live from OpenAlex

In this paper, a developed n-type piezoresistive three dimensional (3D) stress sensor with full temperature compensation is presented. The proposed sensing rosette benefits from the stress insensitivity of the full-circular n-type piezoresistor, oriented over (111) silicon plane, to detect only temperature changes for compensation. Moreover, the unique behavior of shear piezoresistive coefficient (π44) of n-Si is utilized to construct a piezorestive stress sensing rosette over (111) silicon plane that is capable of extracting 3D stress components. A prototype stress sensing chip was microfabricatedto test the capabilityof the developedsensing rosette to accurately extract stress applied on structures at different thermal environments. The fabricated sensing chip was subjected to different mechanical loads using a loading rig with four-point bending fixture. The testing was carried out over a temperature range of 0-50 °C. The results showed that the proposed sensing chip has the capability of capturing the stress applied at different temperatures. Also, the developed sensing rosette showed less sensitivity to the uncertainty in piezoresistive coefficients' values compared to the other developed 3D piezoresistive stress sensors.

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 score0.085
Threshold uncertainty score0.538

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 head0.012
GPT teacher head0.227
Teacher spread0.216 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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