Design-Specific Optimization Considering Supply and Threshold Voltage Variations
Bibliographic record
Abstract
Variations in supply (Vdd) and threshold voltages (Vth) significantly impact parametric yield. These variations also affectVddandVthscaling, two power reduction techniques that effectively reduce dynamic and static power consumption. This paper presents a statistical methodology for maximizing yield and optimizing supply and threshold voltage scaling under theVddandVthvariations. A design-specific feasible region is constrained by a minimum performance and maximum temperature in theVth-Vddplane. A tolerance box is placed in the feasible region so that its center provides the nominal values forVddandVthsuch that the design has a maximum immunity to the variations and maximizes the yield for the given constraints. It is demonstrated that the location of the tolerance box and, therefore, the values ofVddandVthdepend on the design metrics, circuit switching activity, transistor sizing, and the given constraints. Monte Carlo simulations indicate a 25% increase in the yield for 90-nm CMOS technology. In addition, the methodology can be adopted as a variability-aware guideline in a design specific power and performance optimization and is applicable to both continuous and discrete voltage scaling. SPECTRE simulations verify the developed method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".