MétaCan
Menu
Back to cohort
Record W2584682354 · doi:10.1088/1361-6439/aa5dfb

MEMS transducers low-cost fabrication using SU-8 in a sacrificial layer-free process

2017· article· en· W2584682354 on OpenAlexaff
Chang Ge, Edmond Cretu

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFabricationMicroelectromechanical systemsTransducerLayer (electronics)Materials scienceProcess (computing)NanotechnologyOptoelectronicsElectronic engineeringEngineeringComputer scienceElectrical engineeringMedicine

Abstract

fetched live from OpenAlex

Abstract We report novel low-cost and rapid fabrication technologies for the fabrication of movable polymer-based MEMS structures, electrically actuated. Using SU-8 photoresist as structural material, both ordinary and functionalized by conductive fillers (nano-Ag particles and carbon nanotubes), the novel fabrication methods provide simple processes, without the use of any sacrificial layer, for achieving suspended structures (beams and membranes) through either binary or gray-scale photolithography. Experimental validation has demonstrated high yields (over 99% for one of the process flows) in achieving electrostatically actuated microstructures with resonant frequencies in the 0.5–0.8 MHz range, with a stable dynamic behavior tested for a period longer than three months. The technology also confirms that ‘doping’ SU-8 with conductive fillers can preserve its photo-patterning capabilities, while modifying other physical properties (electrical conductivity). As the patterning of SU-8 films can take place on either rigid or flexible substrates, this low-cost technology promises a wide range of applications.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Micromechanics and MicroengineeringSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207