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Record W2110417421 · doi:10.1088/0960-1317/16/3/002

Fuse-tethers in MEMS

2006· article· en· W2110417421 on OpenAlexaff
Yu-Shan Chiu, Kwan-Shi Chang, Robert W. Johnstone, M. Parameswaran

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsJoule heatingFuse (electrical)Joule effectMicroelectromechanical systemsFabricationSubstrate (aquarium)Reliability (semiconductor)Materials scienceElectric heatingProcess (computing)Joule (programming language)SiliconMechanical engineeringStructural engineeringEngineeringComposite materialElectrical engineeringOptoelectronicsComputer sciencePhysicsPower (physics)

Abstract

fetched live from OpenAlex

During the fabrication of freestanding micromechanical structures, the structures must often be attached to the substrate to prevent movement, particularly during the release process. The attachments are then removed, freeing the structures from the substrate when they are to be used. Tethers are long thin beams that mechanically anchor freestanding structures to the substrate during fabrication, but are easily broken afterwards. This paper focused on fuse-tether designs and the associated technique used to break the tethers, Joule heating. The breaking characteristics of two fuse-tether designs were investigated using different current pulses. For each design, the current pulse that produced the most desirable electrical and mechanical break was chosen for reliability testing. The reliability tests resulted in a 100% success rate. However, molten silicon splattered undesirably in 20% of the cases. In addition to empirical testing, ANSYS® was used to simulate the Joule heating process. The ANSYS® model produced results that closely matched the break characteristics observed in the empirical tests. This research demonstrated that a fuse-tether can be severed reliably with the Joule heating technique, and the fuse-breaking characteristics can be predicted by modeling.

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 score0.021
Threshold uncertainty score0.486

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 head0.002
GPT teacher head0.155
Teacher spread0.153 · 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

Citations17
Published2006
Admission routes1
Has abstractyes

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