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Record W2049735291 · doi:10.1117/12.2037344

Design and fabrication of three-axis accelerometer sensor microsystem for wide temperature range applications using semi-custom process

2014· article· en· W2049735291 on OpenAlexaff
Adel Merdassi, Yongjie Wang, George Xereas, Vamsy P. Chodavarapu

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsAccelerometerMicrosystemCapacitive sensingMicroelectromechanical systemsMaterials scienceSurface micromachiningProof massElectrical engineeringFabricationCMOSOptoelectronicsCapacitanceElectronic engineeringComputer scienceEngineeringNanotechnologyPhysics

Abstract

fetched live from OpenAlex

This paper describes an integrated CMOS-MEMS inertial sensor microsystem, consisting of a 3-axis accelerometer sensor device and its complementary readout circuit, which is designed to operate over a wide temperature range from - 55°C to 175°C. The accelerometer device is based on capacitive transduction and is fabricated using PolyMUMPS, which is a commercial process available from MEMSCAP. The fabricated accelerometer device is then post-processed by depositing a layer of amorphous silicon carbide to form a composite sensor structure to improve its performance over an extended wide temperature range. We designed and fabricated a CMOS readout circuit in IBM 0.13μm process that interfaces with the accelerometer device to serve as a capacitance to voltage converter. The accelerometer device is designed to operate over a measurement range of ±20g. The described sensor system allows low power, low cost and mass-producible implementation well suited for a variety of applications with harsh or wide temperature operating conditions.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.234
Teacher spread0.218 · 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
Published2014
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207