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Record W4416879665 · doi:10.37665/weoiqlq94712

LGA Void Reduction via Optimization of Solder Paste Deposition

2022· article· W4416879665 on OpenAlexaboutno aff
Emily Belfield

Bibliographic record

VenueOn-Demand Webinars · 2022
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSolderingCorporationElectronicsIndiumDeposition (geology)BrazingReflow soldering

Abstract

fetched live from OpenAlex

ABSTRACT On-Demand Webinar Originally Hosted by the SMTA Ontario Chapter Technical Presentation: It is no secret that voiding under bottom terminated components continues to be an issue for many electronics manufacturers. A tried and true technique that aids in void reduction focuses on optimizing the solder paste deposition via aperture modification. In this session, Emily will walk us through some case studies in which this technique showed significant voiding improvement under land grid arrays (LGAs). Biography: Emily Belfield is the Northeast Regional Sales Manager for Indium Corporation and is responsible for maintaining existing sales and driving new qualifications and sales through effective account management and coordination of resources. She joined Indium Corporation in 2020 as a Technical Support Engineer where she provided technical assistance to resolve soldering process-related issues. Emily earned her bachelor's degree in Chemical Engineering at Syracuse University. Emily Belfield On Demand Webinar

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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