Electrolytic Cu plating and anti-tarnish influence on Cu Layer oxidation
Bibliographic record
Abstract
In Semiconductor package assembly process, Cu lead frame (LF) must be exposed to high temperatures in die bond, wire bond heat plates before encapsulated, and easy to be oxidized [1], [2], [3], N2 and H2 forming gas is applied to prevent LF surface from oxidation. But we still encountered serious "Cu accumulation" on die bond heat block. Cu accumulation was caused by LF Cu surface oxidation. In this paper Cu oxidation prevention factors were studied together with lead frame suppliers, key factors such as electrolytic copper plating parameters, anti-tarnish types were investigated and optimized.Cu oxidations were thoroughly studied in two aspects. One is anti-tarnish layer protection effect, different anti-tarnish types were compared by actual Cu accumulation performance and follow with dipping process optimization, which was characterized by Cu surface contact angle, X-ray Photoelectron Spectroscopy (XPS).2nd factor is electrolytic Cu layer structure influence on Cu Oxidation, Scanning Electron Microscopy (SEM), Focused Ion Beam (FIB) was used for surface morphology and Cu layer internal structure investigation. Result indicates different Cu layer structure have different oxidation performance. Further in-depth study indicates that different plating chemical solution setup influence Cu layer grain structure, which is the root cause.With this study, key Cu layer oxidation influence factors were identified and optimized, Cu accumulation issue solved.
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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".