A Statistical Design-Oriented Delay Variation Model Accounting for Within-Die Variations
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Bibliographic record
Abstract
The increase of statistical variations in advanced nanometer CMOS technologies poses a major challenge for digital circuit design. In this paper, we study the impact of random variations on the delay variability of a gate and derive simple and scalable statistical models to effectively evaluate delay variations in the presence of within-die variations. The derived models are verified and compared to Monte Carlo SPICE simulations using industrial 90-nm technology. This paper provides new design insight and highlights the importance of accounting for the effect of input slew on delay variations, particularly at lower supply voltages. We also show that, for a given supply voltage, there is an optimum input slew that minimizes the relative delay variation of the gate. We present conditions to achieve this minimum. The derived analytical models account for the impact of supply voltage and output loading and can be used in early design cycle. These results are particularly important for variation-tolerant design in nanometer technologies, particularly in low-power and low-voltage operation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it