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Record W4416878043 · doi:10.37665/smmclpt63960

Evaluating the Manufacturability and Reliability of New Connector Designs

2004· article· W4416878043 on OpenAlexaff
Heather McCormick, George Riccitelli

Bibliographic record

VenueSMTA International · 2004
Typearticle
Language
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsHain Celestial (Canada)
Fundersnot available
KeywordsDesign for manufacturabilityBall grid arrayMotherboardReliability (semiconductor)Cable glandBackupTest (biology)Test plan

Abstract

fetched live from OpenAlex

ABSTRACT As electronic packages decrease in size and pitches decline, daughter cards have become popular methods for creating “islands of density” on larger boards as a means of simplifying the motherboard and reducing their cost. Connector suppliers have responded to this trend by creating BGA type board-to-board connectors. Further, to eliminate the need to wave solder connectors and simplify the process, BGA connectors and press fit connectors have been used. However, there is a need to study the manufacturability of these new connectors and generate full second-level reliability data. This paper will show how test vehicles have been designed to allow these connectors to be evaluated for both manufacturability and reliability, and will outline how IPC-9701 has been used as a guideline in developing test plans. The “Metropolis” test vehicle will be presented, which incorporates BGA mezzanine connectors, a BGA mounted socket, and press fit connectors. Design features included in the “Metropolis” cards to facilitate the planned testing will be discussed. The assembly results for the test vehicle, including yield data, will be reviewed, and future deliverables for the project, such as reliability testing and lead free assembly, will be discussed.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.053
GPT teacher head0.328
Teacher spread0.275 · 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 designObservational
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
Published2004
Admission routes1
Has abstractyes

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