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Record W2741649833 · doi:10.1049/mnl.2017.0270

Wafer‐level vacuum‐encapsulated silicon resonators with arc‐welded electrodes

2017· article· en· W2741649833 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicro & Nano Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCMC MicrosystemsUniversity of Dayton
KeywordsWaferMaterials scienceSiliconResonatorElectrodeOptoelectronicsVacuum arcWeldingArc (geometry)MetallurgyEngineering physicsElectrical engineeringMechanical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

This work presents wafer‐level vacuum‐encapsulated silicon resonators that utilise movable electrodes and arc welding in order to achieve deep sub‐micron transduction gaps. The devices are fabricated using micro‐electro‐mechanical systems (MEMS) integrated design for inertial sensors (MIDIS) process, a commercial pure‐play MEMS process, offered by Teledyne DALSA Semiconductor Inc.. The default minimum transduction gap in the MIDIS process is 1.5 μm. Here, the work introduces a technique to permanently reduce the transduction gap of the resonator using localised arc welding to a designed width of ∼200 nm. The prototype Lamé mode resonators are encapsulated in an ultra‐clean 10 mTorr vacuum cavity that ensures long‐term stability. The quality factor was measured to be 1.37 million at a resonance frequency of 6.89 MHz. With the narrower gap, the motional resistance of the resonators is reduced by a factor of ten times.

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 score0.015
Threshold uncertainty score0.963

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 science0.0010.000
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 head0.011
GPT teacher head0.211
Teacher spread0.199 · 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