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Record W2161188686 · doi:10.1088/0960-1317/14/12/020

Post-packaging frequency tuning of microresonators by pulsed laser deposition

2004· article· en· W2161188686 on OpenAlexaff
Mu Chiao, Liwei Lin

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceLaserPulsed laser depositionOptoelectronicsResonatorFluenceDeposition (geology)Silicon nitrideSapphireSiliconOpticsThin filmNanotechnology

Abstract

fetched live from OpenAlex

This paper presents a post-packaging tuning process for microresonators by PLD (pulsed laser deposition). The microresonators are first hermetically packaged using a RTP (rapid thermal processing) aluminum-to-silicon nitride bonding process. The resonator mass is then altered by adding materials on the surface of the structure using PLD to achieve the desired resonant frequency. The demonstrated tuning resolution is 0.5% per laser shot with a laser beam spot size of 25 × 25 µm 2 and fluence of 616 mJ cm −2 . The laser pulse duration is 6 ns and a 0.35 µm thick gold film is used as the deposition material for comb-drive microresonators resonating at 12.45 kHz.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.001

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 head0.002
GPT teacher head0.169
Teacher spread0.166 · 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

Citations33
Published2004
Admission routes1
Has abstractyes

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