Impact of Lead Free Solders on MLC Flex Capabilities
Bibliographic record
Abstract
Ceramic capacitors have proven themselves very reliable with extremely low failure rates. As processes capabilities and controls continue to improve, reliability of the dielectric also continues to increase, leaving this capacitor type as an extremely reliable selection. This increase in the reliability of the dielectric has created a shift in the categories of main contributors to the overall failure rate. A recent review of field failure analysis of surface mount MLCCs, has shown that “Flex Cracks” account for as much as 40% of ceramic capacitors failures. Flex cracks are created after the component is mounted and a physical displacement of the board generates sufficient stress within the ceramic body to fracture the ceramic material. The generated crack is nearly impossible to detect electrically as the failure shows a time dependence allowing the circuit to be shipped with faulty capacitors on the boards. As the industry moves to lead free soldering systems, which are less ductile, there have been concerns raised concerning the flex capabilities for these new systems. Previous papers have been presented showing that the move to lead-free solders has a detrimental impact on MLC capacitors due to an increase in flex crack occurrences.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".