Strategic Environmental Research and Development Program (SERDP) Tin Whisker Testing and Modeling: Thermal Cycling Testing
Bibliographic record
Abstract
ABSTRACT Driven in part by European Union directives, most commercial electronics manufacturers began a global movement away from using lead (Pb). Component manufacturers are increasingly applying tin-rich finishes to the leads of their devices and soldering with lead (Pb)-free solders. Unfortunately, this can create a risk of tin whisker formation that can result in electrical failures. Because of its unique requirements such as long service lifetimes, rugged operating environments, and high consequences of failure, the aerospace and defense industries must mitigate the detrimental effects that tin whiskers. The present paper provides a status on the effort associated with a multi-year testing and modeling program that aims to assess tin whisker growth on lead-free manufactured assemblies and utilizes whisker short circuit statistical modeling to enable improved reliability assessments. The tin whisker growth of tin finished parts soldered with SAC305 (Sn-3.0Ag-0.5Cu) solder alloy under −55 to +85 °C thermal shock cycling conditions in accordance with JESD-201 for over 2000 cycles was evaluated. Significant whisker nucleation and growth from the SAC305 solder alloy occurred where the solder was less than 25 microns thick with alloy 42 lead terminations. Whisker angular rotation during growth was also observed. Several competing stress relaxation mechanisms in addition to whisker growth were found including excessive deformation, volume recrystallization with massive eruptions of the recrystallized grains. This type of microstructure is not typical for most field conditions. Therefore, the JESD-201 −55 to 85 °C three cycles per hour thermal shock profile does not provide an optimal lead-free assembly whisker assessment test. Also, this test causes cracking of harder conformal coatings not observed in service and may not allow whisker mitigation evaluation of certain conformal coatings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".