Protocol Development for Testing Solder Reliability in Combined Environments
Bibliographic record
Abstract
ABSTRACT This paper describes a proposed method for investigating solder joint reliability in a combined environment of vibration and thermal cycle testing. Since combined environmental testing is an evolving concept, no default approach or standard test protocol currently exists. The need to develop such a protocol arises from the fact that materials may behave differently under combined stress conditions; exhibit different failure modes and impact the overall reliability. Combined environmental testing would therefore provide the closest approximation to actual field conditions and the best means of evaluating the performance capability of solder joints. In developing this protocol, consideration was given to obtain relevant information from both a reliability perspective (number of cycles to failure) as well as micro-structural stand point (at time of failure). Further, in combining the two conditions, time to failure had to be weighed against the overall expected time of the test; when performed alone, vibration testing is often completed within a single day, while thermal cycle testing can take up to six months to complete. Phase one of this project will include developing and refining the test protocol. Phase two will then use this protocol to evaluate, characterize and compare various low melt, Bi-containing alloys against currently used SAC305 and Sn-Pb solders.
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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 | 0.038 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".