Mechanical Characterization of SAC305 and SnPb36Ag2 BGA Assemblies Under Static Flexural Loading
Bibliographic record
Abstract
ABSTRACT Static bending-induced solder joint damage is a main reliability concern for aerospace and military industries whose Printed Circuit Board Assemblies (PCBAs) are required to remain functional under flexural loading. In order to dissipate heat in electronic assemblies, it is common to install thermal gap pads on electronic packages. When compressing thermal gap pads during the fixture process, the PCBA can bend and solder joints can therefore crack if the deflection is too important. This paper reports the durability of 96.5Sn-3.0Ag-0.5Cu (SAC305) and 62Sn-36Pb-2Ag (SnPb36Ag2) Ball Grid Array (BGA) assemblies under static flexural loading at −55°C, 20°C and 125°C. As electronic equipment can be stored at high temperature for prolonged durations, some SAC305 test vehicles were also aged at 125°C for 192 hours. For each test configuration, the bending tests were conducted at a ramp-rate of 2 mm/min and the central displacement-to-failure was measured. Finite Element Modeling (FEM) analysis was conducted considering a global-local approach and the relationship between the central displacement-to-failure and the local PCB strain near the critical solder joints was determined for each test configuration. The results give the necessary data for designers to assess whether a specific PCBA design subjected to static bending is at risk or not.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".