<title>Modeling and properties of hybrid integration structures based on unbalanced nonradiative dielectric (NRD) waveguide</title>
Bibliographic record
Abstract
Non-radiative dielectric (NRD) waveguide, surface-mounted on the top of a relatively thin planar substrate, provides a great flexibility for the integration of planar circuits with NRD-guide. Such an unbalanced NRD-guide may be in direct contact with planar circuits that are fabricated on the same substrate such as microstrip circuits. Besides, NRD-guide may be easily integrated with the microstrip circuits also on a separate dielectric layer by aperture coupling. To facilitate its practical implementation, the dielectric layered surface-mounted NRD-guide structure has been proposed in this paper, this structure is especially suited to the implemented of millimeter-wave integrated circuits. Transitions of planar circuit to surface-mounted NRD-guide have been studied with emphasis on the analysis of potential spurious modes, which provides a basis for the performance-enhanced broadband design and applications. Principal modes generated in the hybrid planar/NRD-guide structure are modeled. Results for transmission and return loss are presented for different transitions. Our investigation indicates than an optimized but uncompensated hybrid planar/NRD-guide integrated transmission should be good enough for many applications over a certain frequency band. For broadband applications, however, spurious mode suppressors in the design of eliminating unwanted modes are required. A design example is also presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".