DEPACT: delay extraction-based passive compact transmission-line macromodeling algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the continually increasing operating frequencies, signal integrity and interconnect analysis in high-speed designs is becoming increasingly important. Recently, several algorithms were proposed for macromodeling and transient analysis of distributed transmission line interconnect networks. The techniques such as method-of-characteristics (MoC) yield fast transient results for long delay lines. However, they do not guarantee the passivity of the macromodel. It has been demonstrated that preserving passivity of the macromodel is essential to guarantee a stable global transient simulation. On the other hand, methods such as matrix rational approximation (MRA) provide efficient macromodels for lossy coupled lines, while preserving the passivity. However, for long lossy delay lines this may require higher order approximations, making the macromodel inefficient. To address the above difficulties, this paper presents a new algorithm for passive and compact macromodeling of distributed transmission lines. The proposed method employs delay extraction prior to approximating the exponential stamp to generate compact macromodels, while ensuring the passivity. Validity and efficiency of the proposed algorithm is demonstrated using several benchmark examples
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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