Delay Optimization in Digital FinFET Circuits Using a Modified Method of Logical Effort
Bibliographic record
Abstract
Delay optimization in digital circuits is crucial, especially at advanced nodes like 12nm, which use FinFET transistors. This thesis extends the classical logical effort method, originally designed for continuous-width transistors (e.g., 65nm CMOS), to optimize delay in FinFET-based circuits. Unlike traditional transistors, FinFETs have discrete width parameters—fins, fingers, and multiplicities—requiring modifications to the standard approach. Two key adaptations are introduced. First, transistor sizing is mapped to discrete FinFET dimensions. Second, a correction factor (Cg/Cd = 1.33) is incorporated to account for the gate-to-diffusion capacitance ratio. These adjustments ensure compatibility with FinFET technology while maintaining accuracy in delay prediction. The optimized method achieves less than 8% error in delay estimation. Simulations show over 20% performance improvement when FinFET sizes are optimized, demonstrating its effectiveness. This framework enables efficient delay optimization, balancing speed and power in FinFET digital circuits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".