SERDP Tin Whisker Testing and Modeling: Simplified Whisker Risk Model Development
Bibliographic record
Abstract
ABSTRACT Most commercial electronics manufacturers began a large-scale movement toward tin rich finishes and solders in 2006 due to European Union Reduction of Hazardous Substances (RoHS) legislation banning lead. Unfortunately, this can create an increased risk of tin whisker induced electrical failures, particularly for defense and aerospace equipment using commercial off the shelf (COTS) items. This paper presents a statistical tin whisker short circuit risk modeling framework for surface mount assemblies having various combinations of tin-lead and lead-free materials. While industry and academia have not developed a robust model correlating whisker length to environmental exposure, the framework does include the results of the multi-year SERDP testing program that is assessing tin whisker growth on lead-free manufactured assemblies in various environments. Since tin whisker length data is expected to mature over the next decade as more measurements are made in the field, a novel technique is employed to facilitate rapid recalculation of short circuit risk as new whisker growth characteristics become available. This is achieved by first determining the geometric lead-to-lead spacing characteristics for various parts. The geometric modeling includes manufacturing variation not readily apparent from the drawings such as printed wiring board conductor spacing reductions due to etching and bulbous solder that decreased conductor-to-conductor spacing. The spacing distributions are then compared to the whisker growth length distribution to determine the probability of a bridging occurrence. Then, the short circuit probability is determined for a given circuit voltage by using NASA data. The computational framework is also used to evaluate the effectiveness of tin-lead hot solder dip and partial conformal coating whisker mitigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".