Board level solder reliability versus ramp rate and dwell time during temperature cycling
Bibliographic record
Abstract
Effects of ramp rate and dwell time are studied through laboratory results and a nts (DOE) using finite element analysis (FEA) incorporating stress/strain and plastic work history. Results demonstrate that solder joint fatigue life is more sensitive to dwell time than ramp rate during thermal cycling. A nonlinear relationship exists between solder fatigue life (mean time to failure, MTTF) and dwell time. Increasing dwell time past 20 min has a minimal effect on the acceleration factor or lifetime. Modeling shows that the acceleration factor increases by a factor of 1.23 for a specific ball grid array (BGA) assembly when the test condition changes from a slow ramp/long dwell (single chamber) to a fast ramp/short dwell (dual chamber). Experiments were performed to validate the FEA modeling. Different ramp time/dwell times were achieved by modulating the temperature profile in a single chamber oven. If dwell time changes from 5 to 10 min with an invariant ramp time of 5 min, apparent MTTF decreases by 55%. However, if the invariant dwell time is 10 min, MTTF remains practically the same as the ramp time increases from 5 to 10 min. These test results are consistent with modeling predictions. The focus of temperature cycling tests should not be on number of chambers, nor upon ramp rate, but upon dwell time. We recommend 8 to 10 min dwell at a high temperature of 125/spl deg/C.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".