Accurate circuit model of interdigital capacitor and its application to design of new quasi-lumped miniaturized filters with suppression of harmonic resonance
Bibliographic record
Abstract
A general-purpose circuit model of a microstrip interdigital capacitor (IDC) is presented in this paper for use in the design of new quasi-lumped miniaturized filters. This computer-aided-design-oriented model is developed as a versatile admittance /spl pi/-network with the short-open calibration technique that we have recently proposed for accurate parameter extraction of a circuit from its physical layout. This technique is self-contained in our method of moments, which accounts for frequency dispersion and fringing effects. A J-inverter topology is further conceived to explicitly formulate the coupling behavior of three types of IDC's. This model provides a unique way for the IDC-related circuit synthesis and optimization based on the accurate equivalent-circuit network extracted from the field theory algorithm. It is validated theoretically and experimentally through an example of a line resonator connected with two IDC's. The proposed scheme is used in the design and optimization of new low-loss miniaturized quasilumped integrated circuits, namely, two types of three-pole direct-coupled bandpass filters. Our measured and predicted results show interesting features of the proposed filter structure such as size reduction and suppression of harmonic resonance if the line resonator is attached by series-connected equivalent inductance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".