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Record W2947525484 · doi:10.1115/1.4043911

Passive Alignment Method for the Bonding of Flat Surfaces Using a Squeeze Flow

2019· article· en· W2947525484 on OpenAlexaff
Willy Lecarpentier, Julien Sylvestre

Bibliographic record

VenueJournal of Applied Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsComputational fluid dynamicsAnodic bondingMaterials scienceWaferPlanarFlow (mathematics)Mechanical engineeringNewtonian fluidFluid dynamicsChipShear stressMechanicsComposite materialComputer scienceNanotechnologyEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

A method to passively align bonded components without direct mechanical contact has been developed. This method uses the pressure field generated by the squeeze flow between the parts during the bonding process to increase the parallelism of planar components. A computational fluid dynamic (CFD) model has been developed to study the squeeze flow phenomenon and to determine generated efforts. Based on these calculations, an assembly stage standing on a flexure pinned linkage has been developed. This assembly stage had two purposes. The first was to show the possibility of passive mechanical alignment using a squeeze flow. The second was to measure efforts to confirm the CFD model. These measurements have led to a refined CFD model taking into account the non-Newtonian behavior of the fluid at high shear rates. This technique was initially developed for the assembly of a fiber-optic-to-silicon-chip-interface. Other potential applications could be wafer bonding, bonding of multiple wafer stacks, or 3D integrated circuits.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score0.360

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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractyes

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