High Speed Copper Plating Process for IC Substrate
Bibliographic record
Abstract
The integrated circuit (IC) substrate occupies an interesting place in electronic devices. They serve as the bridge between the integrated circuits and the PCB. In this connecting point, they must meet the requirements of both worlds. They must provide the high density of connections to match the IC chip, while at the same time be a stable and robust platform for the chip to reside on. The IC substrate feature sizes and uniformity requirements are moving closer to what is required in the semi-conductor industry, except these substrates must be made at a scale, speed, and cost expected in the PCB industry. In this paper, we present an acid copper electrolyte for plating 2 in 1 redistribution layers (RDL), with small vias and fine lines down to 10µm wide. The electrolytic process successfully plates these features, while maintaining a uniform surface across the panel. The 510 mm x 515 mm panel had less than 5% variation in Cu thickness. The electrolyte was evaluated in a vertical continuous plating (VCP) style tool and a tool that plates panels at high speed. The standard VCP tool operated around 1.0 – 3.0 ASD, whereas the high-speed plater operated around 4.5 – 6.5 ASD. The obvious benefit of the high-speed tool was to reduce the plating time, but it also provided new degrees of control that are not available in VCPs. In either tool, the electrolyte performed well and produced uniform deposits that met the physical requirements that IC substrate applications demand.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".