A Nanocopper Based Alternative to High Temperature Solder
Bibliographic record
Abstract
ABSTRACT Alternatives to high temperature solder remain limited. The CuantumFuse™ material under development by the Advanced Technology Center at the Lockheed Martin Corporation appears to be the only one to offer the potential for a drop-in replacement of solder as far as SMT assembly equipment and processes are concerned. The Cu nanoparticle paste is printable with the consistency of solder paste and the particles fuse together at 200oC in a conventional full convection reflow oven. The resulting Cu joint is nano-crystalline and nanoporous, lending it some unique properties, and the microstructure is not thermally stable but the joint remains solid up to higher temperatures than envisioned in any microelectronics application. Assessment of the reliability of existing joints, not to mention the optimization and prediction of the reliability of new versions of the material, will require much more than just accelerated testing. Different versions of the nano-Cu material are being characterized in terms of creep rates and mechanisms as well as the behavior in both thermal and isothermal cycling. Microstructures are characterized by FIB polishing, to preserve porosity structures, followed by optical microscopy, SEM, and TEM. Generalization of results requires us, among other, to distinguish between competing effects of the distributions of grain sizes and pores on creep. Experiments were extended to include nano-porous Au samples with much larger grain sizes to help resolve that. In general, counteracting effects of grain sizes and pore distributions on ductility, strength and fatigue resistance may offer opportunities for optimization of nano-particle based joint structures. So far we argue that comparisons of the present nano-Cu material to solder in accelerated thermal or isothermal cycling will tend to be extremely conservative.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".