A Robust Integrated Power Delivery Methodology for 3-D ICs
Bibliographic record
Abstract
The inherent advantages of three-dimensional (3-D) integrated circuits (ICs) are well-aligned with the continuous demand for increased density of functionality, reduced latency, the power dissipation of communication, and heterogeneity of modern applications. Delivering power efficiently to highly heterogeneous voltage domains across the tiers of a 3-D IC is, however, a significant challenge. To address the power delivery challenge in 3-D ICs, a robust integrated power delivery methodology is proposed in this article. Recent advancements in the fabrication of high-density integrated passive components, and the area that is available in the vertical dimension of the 3-D construct, are exploited in this work to enable an efficient and robust power delivery system for 3-D ICs. In the proposed approach, one or more layers within the 3-D structure are dedicated to power conversion and regulation, namely, power layers (PLs). A design exploration stage is also provided to determine the number of PLs, distribution of resources between power and functional layers (FLs), assignment of voltage domains to PLs, and voltage levels across the power delivery system. The proposed methodology is compared to three other power delivery topologies and exhibits 1.4–$38\times $and 1.4–$7.1\times $improvement in, respectively, voltage drop and power efficiency. Results are normalized to the total on- and off-chip area dedicated to power conversion and regulation in each topology.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".