Tin Whisker Growth and Mitigation with a Nanocrysytalline Nickel Coating
Bibliographic record
Abstract
Tin Whisker Growth and Mitigation with a Nanocrysytalline Nickel Coating Master of Applied Science, 2015 Szymon Janiuk Materials Science and Engineering, University of Toronto Abstract: Tin whiskers are a problem in the electronics industry since the EU banned the use of lead in Pb-Sn solders as part of the Restriction of Hazardous Substances (RoHS). The biggest concern with Sn whiskers is their ability to short-circuit electronics. High reliability applications such as the aerospace, defense, healthcare, and automotive industries are at most risk. This project explores Sn whisker mitigation and prevention with the use of nanocrystalline nickel coating over Sn surfaces. Sn was plated onto a pure Cu substrate using electroplating. A high temperature and high humidity condition, at 85°C and 85% RH, was effective at growing whiskers. A nNi coating was plated over Sn/Cu coupons. After subjecting the nNi/ Sn/Cu samples through 85°C/85% RH testing conditions, no whiskers were observed penetrating the surface. These results make nNi a viable material to use as a coating to prevent the growth of Sn whiskers in electronic assemblies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".