Bibliographic record
Abstract
Efficient power delivery is a critical enabler for the future of three-dimensional integrated circuits (3D ICs). To this end, on-chip power demand, impedance of power delivery paths (on- and off-chip), and heat dissipation, need to be considered simultaneously. Given the high off-chip parasitics, power conversion within the 3D stack (on-chip) is a promising solution to overcome key power delivery challenges in 3D ICs. Recent developments in fabrication of high-density on-chip passive components have paved the way for the implementation of fully integrated voltage regulators (FIVRs). This work builds on the FIVR approach to propose an efficient integrated power delivery methodology for 3D ICs.In the proposed methodology, depending on the number of layers and the power characteristics of each layer, one or more layers of the 3D structure are dedicated to power conversion, regulation, and management. A case study of a five-layer 3D IC is considered where the proposed methodology is compared with conventional and FIVR-based approaches under, both, static and transient conditions. The proposed methodology exhibits a reduction in power loss and voltage drop of, respectively, 5X and 24X, while occupying a significantly smaller horizontal area, as compared to the other approaches.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".