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Record W3174248627 · doi:10.1088/1361-6439/ac0fbf

Silicon MEMS inertial sensors evolution over a quarter century

2021· article· en· W3174248627 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMicroelectromechanical systemsQuarter (Canadian coin)Inertial frame of referenceSiliconSilicon valleyInertial measurement unitEngineeringElectrical engineeringOptoelectronicsAerospace engineeringMechanical engineeringMaterials sciencePhysicsGeographyBusiness

Abstract

fetched live from OpenAlex

Abstract Silicon-based microelectromechanical systems (MEMS) inertial sensors have become ubiquitous, revolutionizing motion sensing, vibration sensing and accurate positioning in several societal fields. Driven by consumer and automotive markets, companies involved in this technological development focused mostly on low cost, miniaturization and low power consumption, somewhat sacrificing measurement accuracy. In several laboratories all over the world, however, the research toward higher-performance sensors has been going on for more than two decades, with the goal of improving two key parameters for future applications: noise density and bias stability. This review article summarizes, for silicon-based MEMS accelerometers and gyroscopes, the most relevant working principles that appeared in the scientific literature. The collection of several data about the above mentioned key figures enables tracing the roadmap for further developments in the upcoming decade.

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.

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 score0.020
Threshold uncertainty score0.656

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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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