Analysis of dc simulation convergence of nonlinear analog circuits with initial solution
Bibliographic record
Abstract
The CPU time spent to repeatedly simulate a circuit with slight variations in the parameters is generally high, even if full accuracy is not required. This paper proposes a method to end the Newton-Raphson (NR) iterative algorithm before convergence in DC analysis in order to reduce the number of NR iterations. In the case of an initial solution approximation is used, the analysis of the NR algorithm behaviour until convergence is presented in order to approximate the accuracy of the solution at each iteration. We show that the use of a large SPICE reltol parameter value is a way to specify a desired accuracy, allowing reducing the number of NR iterations, as a time/accuracy trade-off. Experimentally, 14%-65% reduction in terms of NR iterations is obtained for DC simulation, compared to the usual SPICE simulation until convergence. Our method is particularly efficient in the case of slightly nonlinear circuits since the initial solution guess is generally accurate. The method is intended to some applications requiring multiple simulations of the same circuit with parameter modifications, such as automatic sizing, fault simulation and Monte-Carlo analysis, in which a solution approximation is available from previous simulation results and for which full accuracy is not required.
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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) | 0.000 | 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 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".