Nonuniformly Distributed Electronic Impedance Synthesizer
Bibliographic record
Abstract
Benefiting from advantageous features of fast tuning, small size, and easy integration, the electronic impedance synthesizer (EIS) has been developed for on-wafer load-pull characterization systems, tunable matching networks, reconfigurable devices and systems, and so on. The previous designs of the EIS were mostly based on the empirical data instead of a closed-form design. Moreover, incomplete figures of merit (FOM) were chosen to optimize and evaluate the EIS. In this paper, we propose and present, first of all, a semiclosed-form design procedure for the distributed EIS. Then, a particle swarm optimization method is introduced to optimize the proposed nonuniformly distributed EIS, which comprises an adjusting circuit and a nonuniformly distributed circuit. Experimental results demonstrate that the proposed nonuniformly distributed structure can not only improve the Smith chart coverage but also reduce the size, compared to the uniform counterpart. The fabricated nonuniform EIS operating from 0.8 to 2.5 GHz, exhibits a good agreement between theory and measurement. Furthermore, the most comprehensive FOM are presented to evaluate the fabricated EIS.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".