Microstructural Evolution During Electromigration in Eutectic Tin-Bismuth BTC Solder Joints
Bibliographic record
Abstract
ABSTRACT Eutectic tin-bismuth alloy and its variations have been identified as the solders of choice for applications that require low-temperature assembly, even though they pose serious metallurgical challenges. Their high homologous temperature results in the solder microstructure constantly changing and the high electromigration effective valence of bismuth results in a thick, relatively brittle bismuth layer accumulating on the anode side of the solder under current stressing. In addition, as in any tin-based alloy joining to copper surfaces, the intermetallic growth too needs to be considered. Two iNEMI teams have been researching Sn-Bi alloys. One has focused on the assembly process and solder joint mechanical reliability aspects and the other on electromigration. Now, in the next phase, the two are being combined to study the effect of electromigration on the mechanical and electrical properties of the solder joints. We have recently completed and published work on the electrical effects of electromigration over a range of temperature that when plotted on an Arrhenius plot can be used to predict the electromigration life of bottom-terminated-component (BTC) eutectic Sn-Bi solder joints under any application condition. In this paper, we report on the microstructural evolution of BTC eutectic Sn-Bi solder joints during electromigration in terms of bismuth redistribution and accumulation at the anode end, and the intermetallic growth on copper and nickel substrates. These findings will greatly aid in the design of experiments to research the effects of electromigration on the mechanical properties of Sn-Bi solder joints.
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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 | 0.000 | 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".