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Record W4416880557 · doi:10.37665/jsmtkpxid94403

Plasma Stencil Treatments: A Statistical Evaluation

2013· article· W4416880557 on OpenAlexaff
William H. Green, Marie Cole, Ruediger Kellmann

Bibliographic record

VenueJournal of Surface Mount Technology · 2013
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsPrinted circuit boardSolder pasteFlip chipStencilSolderingSurface-mount technologyCircuit reliabilityChipElectronic componentReflow soldering

Abstract

fetched live from OpenAlex

ABSTRACT As printed circuit board complexities continue to increase, packing more functionality into smaller physical dimensions has become increasingly important. As a result, a wider variety of electronic components are being incorporated into product bills of materials. Large body ASICs (logic) and sub-system docking connectors generally drive large SMT pad designs, while fine pitch devices such as flip chip QFNs, 0402, and 0201 chip passives are now commonplace within electronic circuitry. Integration of these large and small body components onto a single printed circuit board assembly (PCBA) with ever increasing population densities and tighter placement spacings, drives the need for consistent solder paste print deposits to ensure maximum first pass assembly yields and highest product quality / reliability levels. Balancing solder paste printing of large and small print deposits has been reported to be enhanced using various surface treatments on laser cut stencils. This study focused on examining the effects of a plasma coating compared to conventional stainless steel (SS), laser cut technology. Printing performance on a variety of components was evaluated including five different BGAs, flip chip QFNs, SMT electrolytic capacitors, 0805, 0402, and 0201 chip passives. Lead-free no clean and water soluble paste chemistries were included, along with two different aperture ratio (A/R) design points for all components studied. For assessing the durability of the plasma coating, production volume cleaning simulations were conducted with twenty four different solvents. Statistical analysis was conducted to evaluate any observed differences between conventional stencil technology and a plasma treated alternative. A design of experiments (DOE) was conducted to evaluate main effects and interactions, helping to make data generated decisions to answer the question: Do plasma treated stencils offer benefits over conventional technology stainless steel laser cut stencils?

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)0.022
metaresearch head score (Gemma)0.040
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.251
Teacher spread0.234 · 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
Published2013
Admission routes1
Has abstractyes

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