Copper Corrosion Effects from Cleaning Agent Entrapment
Bibliographic record
Abstract
ABSTRACT With the increasing complexity and decreasing size of electronic assemblies, the cleanliness of electronics is becoming increasingly critical to the reliability of the final assembly. Contaminants, such as flux, left from the assembly process can cause electrochemical migration (ECM), or conductive anodic filament (CAF), current leakage, or simple corrosion, all of which are detrimental to the reliability of the product. To mitigate these risks, electronics manufacturers are increasingly turning towards cleaning to solve these and other issues. Most commonly, the completed assembly is cleaned at the end of the manufacturing processes, although it is becoming more common to clean at certain critical stages during the manufacturing process in addition to at the end of the manufacturing process. There are several cleaning options available, such as vapor degreasing, semi-aqueous cleaning, and aqueous cleaning. Aqueous cleaning is the predominate cleaning process in the United States, and consists of diluting a cleaning agent with water and using the cleaning agent-water mixture to clean the assembly usually, sprayed in air in an in-line cleaning machine and then rinsed with water. With increasingly complicated and small electronic components, there are ample opportunities for the cleaning agent, with the dissolved contaminants, to become entrapped, such as in connectors, inductors, and in vias. These trapped residues become dried onto the assembly. There is increasing concern that these residues can contribute to corrosion of exposed copper in the assembly or may contribute to the aforementioned failure modes. A study was conducted to ascertain the corrosion rate and products of cleaning agent residues and cleaning agent with flux residue on copper to assess the risk to the completed assembly. Corrosion rates were measured and the corrosion products were analyzed by Scanning Electron Microscopy Energy Dispersive X-ray spectroscopy (SEM-EDX).
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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 | 0.001 |
| 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".