MétaCan
Menu
Back to cohort
Record W2058163584 · doi:10.1109/icsens.2013.6688275

Design of CMOS capacitance to frequency converter for high-temperature MEMS sensors

2013· article· en· W2058163584 on OpenAlexafffund
Yucai Wang, Vamsy P. Chodavarapu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSCapacitanceCapacitive sensingElectrical engineeringElectronic engineeringParasitic capacitanceSIGNAL (programming language)Computer scienceEngineeringPhysicsElectrode

Abstract

fetched live from OpenAlex

We present a CMOS capacitance to frequency convertor for capacitive MEMS sensors working in high temperature environments. Many MEMS sensors are needed to work at the extended military temperature range from -55oC to 175oC and require a suitable readout circuitry to detect, process, and transmit the sensor measurement data. The proposed CMOS circuitry uses a current to frequency conversion circuit to convert the sensor capacitance output into a digital output modulated in frequency. Pulse width control is implemented on-chip to control the pulse width of the output digital signal. The circuitry is implemented using IBM 0.13μm CMOS technology. Simulation results show that the circuitry has excellent stability over wide temperature range, good accuracy and high sensing resolution. The novelty in this work lies in the fact that a digital output is achieved without using complex analog to digital converter (ADC).

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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.011
GPT teacher head0.202
Teacher spread0.191 · 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
GenreMethods

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
Published2013
Admission routes2
Has abstractyes

Explore more

Same topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207