A Geometry and Size Independent Failure Criterion for Fracture Prediction in Lead-Free Solder Joints
Bibliographic record
Abstract
ABSTRACT Solder joint fracture due to mechanical loads, such as drop impact and board bending, is a significant reliability concern, but little attention has been paid to the development of methods of predicting solder joint fracture under such loading conditions. This paper evaluates a failure criterion that can predict joint failure independent of joint size and geometry. In the first part of the work, continuous and discrete SAC305 solder joints of different lengths were made between copper bars using standard surface mount processing conditions, and then fractured under various loading combinations: pure tensile stress, mixed tensile and shear stress. The critical loads corresponding to crack initiation in the continuous joints were measured and used to determine the fracture parameters at initiation, G ci and J ci . This serves as a strength property for the solder-substrate system. In the second part of the investigation, fracture of discrete solder joints was simulated using elastic and elasticplastic finite element methods, and crack initiation was predicted using the measured G ci and J ci values for this solder system. The predictions matched reasonably well with the measured values. An interesting observation was that the failure of joints less than 2 mm in length can be predicted using the fracture parameters at initiation from continuous joints. This suggests that G ci and J ci measured in this way should also provide a strength property that is applicable to failure prediction in much smaller microelectronic joints. In fact, some preliminary fracture predictions of solder balls in a PBGA package showed a good agreement with experimental observations. The scope of this study was limited to quasi-static loading, but the methodology can be extended to impact and hightemperature loading conditions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".