Sac and SnPb Solder Joint Thermal Stress and Strain Characterization for Resistor Packages
Bibliographic record
Abstract
ABSTRACT Two similar resistor packages, one with SAC alloy (Sn95.5Ag3.8Cu0.7) joints and the other with eutectic tinlead joints, are tested under similar thermal profiles. The total strains in the joints are measured against temperature variation by applying Digital Speckle Correlation (DSC) technique. With the measurements obtained in an area of about 20 by 30 μm near the corner of solder fillet, the average strain is calculated in each case. Time, temperature, and stress dependent deformation of the interconnect materials are analyzed based on the materials' constitutive laws. A strain partitioning method is devised to separate the elastic, plastic and creep components and to solve for the shear stress. The results revealed similar stress and strain characterizations and trend of variation with temperature for both solder joints. Creep is the dominant constituent in the total strain. Stress relaxation begins before dwelling starts and continues through the rest of the testing. The study also shows that SAC alloy solder joint experiences higher stress, strain and strain rate as compared with tin-lead one. The stress-strain curves plotted for both joints also show higher strain energy absorption in SAC solder joint than in tin-lead one.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".