A Generalized Circuit Model Development Approach With Short Open Thru (SOT) De-Embedding Technique and Its Applications
Bibliographic record
Abstract
A short-open-thru (SOT) numerical de-embedding technique is proposed and studied in this work. In particular, a generalized methodology for circuit model development is derived for the extraction of accurate circuit parameters over a wide range of frequency. The entire de-embedding process is described, and the circuit model development strategy is explained step-by-step. A variety of electrically small planar circuit elements, such as microstrip line (MSL) gap discontinuities, step discontinuities, and via-holes in two-layered substrate discontinuities, are numerically de-embedded and extracted conventional circuit model parameters are compared with results generated by a recently published short-open-load (SOL) technique. In addition, the circuit parameters extracted by the proposed generalized decomposition technique are comparatively studied through both SOT and SOL methods. The outcomes confirm that the circuit parameters extracted by the proposed circuit model has better model behavior over a wide range of frequency as opposed to those coming out of its conventional counterpart. Furthermore, the SOT technique-based circuit parametrization provides better stability as compared to the SOL scheme. Numerical convergence over a wide range of frequency is demonstrated for each example. Finally, a third-order Chebyshev end-coupled filter is designed by the proposed technique. Its equivalent circuit model, full-wave electromagnetic (EM)$S$-parameters simulation, and measured results have validated the approach.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".