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Record W1981684536 · doi:10.1109/iscas.2010.5537914

A MEMS-based temperature-compensated vacuum sensor for low-power monolithic integration

2010· article· en· W1981684536 on OpenAlexaff
Mohammad Taghvaei, Paul-Vahé Cicek, Karim Allidina, Frédéric Nabki, Mourad N. El-Gamal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicroelectromechanical systemsTransimpedance amplifierMaterials scienceResonatorCMOSElectrical engineeringAmplifierOptoelectronicsPower (physics)Electronic engineeringOperational amplifierEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a MEMS resonator-based vacuum sensor with a low-power transimpedance amplifier and a mixer-based frequency-to-digital converter. The MEMS resonator is fabricated in a CMOS-compatible process, and a 130 nm CMOS technology is used to design the integrated circuitry. The vacuum sensor operates in the pressure range from 10 to 1200 mbar with a resolution of ~2 mbar. The system is temperature-compensated between -10°C and 60°C. The simulated power consumption of the entire system is less than 495 μW from a 1 V supply.

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 score0.019
Threshold uncertainty score0.544

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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
Published2010
Admission routes1
Has abstractyes

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