Vacuum-Assisted Through Silicon via Filling Method With Ag-Based Epoxy
Bibliographic record
Abstract
In the field of 3-D integration, Cu electroplating has become the most popular method to metallize the through silicon via, due to the higher conductivity of Cu compared to W and doped poly-silicon and the good compatibility with other microfabrication procedures. However, many problems embedded in this technique, such as long deposition time, relatively high complexity, and environmental pollution, are still unsolved. In this paper, we utilized the Ag-based epoxy to fill the vias with the assistance of vacuum pressure to solve all these problems. It was found that the vacuum level played a more important role than the suction time in this process, as the filling depth for the vias with a diameter of 100 μm and a depth of 500 μm grew more visibly in a fixed lifespan when the vacuum pressure elevated from 0.2 to 1.6 kPa, and to realize a 100% filling ratio, 1.6 kPa and 3 s would be needed at least. By running the basic two-point probe test for two times without any printed circuit board, the volume resistance of fully filled vias was measured, and the results indicated that the average resistance was ~25 Ω. During the temperature increase from room temperature to 120°C, this material established good stability in resistivity as the change in via resistance was negligible. The adhesion quality between this material and Cu-based bonding pad was tested as well, and the result was acceptable.
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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) | 0.000 | 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".