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Record W4416877931 · doi:10.37665/smxohft12166

Materials Testing of PWB Substrates to Determine Survivability Through Lead Free Assembly

2013· article· W4416877931 on OpenAlexaff
Bill Birch, Jason Furlong

Bibliographic record

VenueSMTA International · 2013
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsPrinted circuit boardReliability (semiconductor)SurvivabilityElectronic packagingDegradation (telecommunications)Circuit reliabilityCapacitanceMaterial properties

Abstract

fetched live from OpenAlex

ABSTRACT As part of High Density Packaging Users Group (HDPUG) Pb-Free Board Materials Reliability Project 3, material testing was performed on both IST test coupons and WIC- 20 test coupons to determine there survivability through Pbfree reflow. The two techniques use similar principles of measuring capacitance change to determine levels of degradation within the B and C stage dielectric materials. The study compared 24 different Pb-free printed wiring board materials in 20 layer constructions, built in a single printed wiring board (PWB) manufacturing facility. Twelve materials are investigated with 2 different glass styles and resin contents, for a total of 24 different builds. The materials in the test included high Tg, filled FR4 materials, high Tg halogen free FR4 materials, and high speed materials. Data is presented showing the impact of each assembly SMT reflow cycle, relative to the location and magnitude of material degradation (delamination). An important aspect of this study compared the performance differences for a via to via spacing of 1mm/0.040” and 0.8mm/0.032”. The results confirmed a major influence from what is considered a relative small design change. The electrical results were compared to traditional microsection analysis to demonstrate the levels of correlation achieved.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.269
Teacher spread0.224 · 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.

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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