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Record W2073533773 · doi:10.1109/lascas.2014.6820298

Cavity formation in bonded silicon wafers using partially cured dry etch bisbenzocyclobutene (BCB)

2014· article· en· W2073533773 on OpenAlexafffund
Aref Bakhtazad, Rayyan Manwar, Sazzadur Chowdhury

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversity of WindsorWestern University
FundersCMC Microsystems
KeywordsMaterials scienceWaferSilicon on insulatorWafer bondingMicroelectronicsOptoelectronicsMicroelectromechanical systemsSiliconCapacitive micromachined ultrasonic transducersCapacitive sensingUltrasonic sensorDry etchingEtching (microfabrication)Composite materialLayer (electronics)Electrical engineeringPiezoelectricityAcoustics

Abstract

fetched live from OpenAlex

A method of forming hidden cavities in bonded silicon wafers using dry etch Bisbenzocyclobutene (BCB) is presented. The cavities are formed by vacuum bonding of partially-cured patterned BCB over a Silicon on Insulator (SOI) wafer and over a bare silicon wafer in a low temperature process. The vacuum bonding process parameters are determined through an iterative process that involves SEM inspection of the BCB layer at the bonding surface to ensure a void and wrinkle free strong bond. The cavities can be as small as 28 μm wide with a support margin of only 10 μm and a height of 800 nm. Cavities of other dimensions can also be realized following the same procedure. The cavities can be used to realize MEMS microphones, capacitive micromachined ultrasonic transducers (CMUT), resonant cavities, and also for protective encapsulation of MEMS and microelectronic dies.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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Same topic3D IC and TSV technologiesFrench-language works237,207