Advanced Framework for Transient Simulation of High-Speed Circuits and Interconnects Using L-Stable High-Order Integration Methods
Bibliographic record
Abstract
The trends toward higher operating frequencies and sharper rise times coupled with faster switching signals have made modeling and simulation of high-speed interconnects an increasingly challenging task.Typically, the circuit models arising from characterizing coupled high-speed interconnects are very large linear circuits with many linear components, making the transient analysis a cumbersome task.Recently, waveform relaxation methods based on transverse partitioning (WR-TP) were proposed to address this issue.It was shown that the complexity of WR-TP grows only linearly with the number of lines.However, as the coupling between the lines becomes stronger, the WR-TP algorithm either fails to converge or the number of iterations required for convergence increases.To address the issue of convergence in WR-TP, an overlapping partitioning (OP) method is presented in this thesis proposal.Using the OP approach, the coupled interconnects are partitioned such that each subcircuit contains one or more lines that are tightly coupled, while allowing the lines between adjacent subcircuits to overlap.The weak coupling between the subcircuits are represented using voltage/current sources.Next, each subcircuit is simulated independently using a suitable integration method for the whole time of interest.The voltage/current sources representing the i ii coupling between the subcircuits are then updated.This process is repeated until convergence is obtained.Typically, during the simulation of each subcircuit, an integration method is used to discretize the time points by taking finite time steps to approximate the waveforms at those time points.Recently, it has been shown that using a high-order and L-stable integration methods based on Obreshkov formula (ObF) can lead to a large reduction in the transient analysis time of general circuits.In this thesis, we show that by using ObF and taking advantage of the special structure of the mathematical formulation of the interconnect circuits, the simulation time of each subcircuit can be significantly reduced.Thus, reducing the overall simulation time.Finally, the ObF is applied to another class of simulation methods, known as Envelope-Following (EF) methods.The EF method is suitable for transient analysis of highly oscillatory circuits with widely separated time-scales.We show that using the high-order ObF leads to a significant reduction in the computational time compared to low-order EF methods because it allows taking large steps in time while keeping the same accuracy.In addition, the new ObF-based EF method is modified to exploit multicore machines to provide fast simulation.I would like to express my gratitude to my co-supervisors, Prof.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".