A Self-Aligned Structure based on V-groove for Accurate Silicon Bridge Placement
Bibliographic record
Abstract
As a lower cost alternative to silicon interposers with through silicon vias (TSVs), silicon bridges have been developed for high-performance computing (HPC) and/or heterogeneous integration. One of the most important steps of this advanced packaging technology is the accurate placement of the silicon bridge die with the organic substrate or with the dies requiring the high-density interconnections. In this paper, we demonstrated a novel mechanism for the straightforward and accurate insertion of silicon bridges into substrates. More specifically, mechanical fiducial in the form of V-groove were etch into the back side of functional bridges comprising Cu pillars, high-density Cu traces and insulating layers on their front side. Correspondingly, 4 SAC305 solder spheres were attached onto the substrate through thermal reflow to anchor the bridge. During the silicon bridge placing process, these 4 couples of concave and convex structures would pair and align each other automatically, determining the x, y and z final position of the silicon bridge. Experimental results showed that with such self-aligned structures, the placement drift of the silicon bridge could be confined to 2.5 μm.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".