C4NP technology: Manufacturability, yields and reliability
Bibliographic record
Abstract
As a part of IBM movement from Pb-rich solders to Pb-free solder, a new low cost process has been developed to deposit the solder to a capture, or under bump metal (UBM) pad, with Suss MicroTech Inc as the equipment partner. The controlled collapsed chip connection new process (C4NP) has moved, over the last 2 years, from development into manufacturing for 300 mm wafers. During this transition, a great number of process improvements have resulted in high fabrication yields. Manufacturing robustness has been achieved by clearly identifying the processes which affect the C4 structural integrity. The solder composition has been optimized to improve its mechanical properties as well as low alpha emission rate requirement. Sector partitioning methodology was used to obtain root cause for various defects which then, through replication studies, were confirmed. Key process improvements in the capture pad build, mold fabrication, and mold fill tool have been accomplished as the process has matured. Thermal undercut was identified as a mechanism of Cu seed consumption when no top Cu was available on top of the Ni UBM. C4NP technology can produce yields comparable to that of electroplated C4 Bumps. Yield learning model shows a 15% defect reduction per month since the start of the C4NP program. Technology qualification for 300 mm wafers with 200um and 150 um pitch Pb-free C4 bumps has been successfully completed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".