Mitigating the Impact of Process Variation on the Performance of 3-D Integrated Circuits
Bibliographic record
Abstract
Three-dimensional die-stacking architectures have been proposed as a promising solution to the increasing interconnect delay that is observed in scaled technologies. Although prior research has extensively evaluated the performance advantage of moving from a 2-D to a 3-D design style, the impact of process parameter variations on 3-D designs has not been studied in detail. In this paper, we attempt to bridge this gap by proposing a variability-aware design framework for fully synchronous (FS) and multiple clock-domain (MCD) 3-D systems. To mitigate the impact of process variations on 3-D designs, we propose the variability-aware 3-D integration strategy for MCD 3-D systems that maximizes the probability of the design meeting specified system performance constraints. The proposed optimization strategy is shown to significantly outperform the FS and MCD 3-D implementations that are conventionally assembled, for example, the MCD designs assembled with the proposed integration strategy provide, on average, 44% and 16.33% higher absolute yield than the FS and conventional MCD designs, respectively, at the 50% yield point of the conventional MCD designs.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".