Transistor-Level Static Timing Analysis by Piecewise Quadratic Waveform Matching
Bibliographic record
Abstract
While fast timing analysis methods, such as asymptotic waveform evaluation (AWE), have been well established for linear circuits, the timing analysis for non-linear circuits, which are dominant in digital CMOS circuits, is usually performed by a SPICE like, time domain integration based approach, involving expensive Newton Raphson iterations at numerous time steps. In this paper, we propose a new technique that leads to the transient solution of charge/discharge paths with a complexity equivalent to only K DC operating point calculations, where K is the number of transistors along the path. This is accomplished by approximating each nodal voltage as a piecewise quadratic waveform, whose characteristics can be determined by matching the charge/discharge currents. Experiments on a wide range of circuits show that a 31.6 times speed-up over SPICE transient simulation with 10ps step size can be achieved, while maintaining an average accuracy of 99%.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".