Two Methods of Evaluating a Printed Wiring Board’s Dielectric Performance in a Lead Free Assembly Environment
Bibliographic record
Abstract
ABSTRACT It has been demonstrated that lead-free assembly and rework of printed wiring boards (PWBs) can reduce reliability by up to 50%, in well fabricated product. There are two main reliability influences in lead-free applications, copper quality and material robustness. The reliability impact of copper quality is readily evaluated using thermal cycling, while monitoring resistance in test circuits, followed by a microscopic evaluation of plating variables and failure analysis. The reliability impact of materials, however, is not so directly evaluated. Although materials impart the z-axis expansion that causes failure, the material itself is not normally monitored in reliability testing. Traditionally material damage, in reliability testing of bare PWBs has been limited to random microscopic evaluation. Materials traditionally considered robust are now failing in a lead-free application. In response to the need to quantify the material’s role in reliability PWB Inc. developed two unique material test methods; cyclic time to delamination at 260°C (cT260) and detection of material degradation in representative test coupons by Dielectric Estimation and Laminate Analysis Method (DELAM). This article offers an overview of these two evaluation methods, their applications, and benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".