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A wafer-level process for bulk tungsten integration in MEMS vibration energy harvesters and inertial sensors

2017· preprint· en· W2743210435 on OpenAlexaff
Andre Dompierre, Luc G. Fréchette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsWaferMicroelectromechanical systemsResonatorTungstenMaterials scienceFabricationSiliconVibrationOptoelectronicsSubstrate (aquarium)CantileverWafer-level packagingCleanroomNanotechnologyAcousticsComposite materialMetallurgyPhysics

Abstract

fetched live from OpenAlex

This paper presents a MEMS fabrication process to integrate high density proof masses made from 500 μm thick tungsten substrates with silicon at the wafer level. Cantilevers are fabricated in a silicon substrate whereas the tungsten masses are wafer bonded and patterned by a 2-step wet chemical milling approach compatible with many common cleanroom materials. Out of plane resonators were fabricated and characterized, exhibiting a resonant frequency of 87 Hz and a Q-factor of 267. This approach enables an array of potential applications for highly sensitive inertial sensors as well as vibration energy harvesters driven by low frequency ambient vibrations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0010.000
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 head0.053
GPT teacher head0.279
Teacher spread0.226 · 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.

Study designSimulation or modeling
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

Citations2
Published2017
Admission routes1
Has abstractyes

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