Leaky-wave radiating surface on heterogeneous high-κ material for monolithic antenna-frontend integration
Bibliographic record
Abstract
In a highly integrated analog radio-over-fiber transceiver, seamless integration of the antenna-frontend is crucial as an antenna is generally implemented on a high-κ material, which is set to highly degrade the antenna's performance. This work is concerned with the radiation behavior improvement of a planar leaky-wave antenna with an inductive partially reflecting surface (PRS) on a high-κ substrate for the development of a highly directive antenna. To begin with, we show how a thin and single-mode resonance (SMR) inductive PRS on high-κ materials in a planar leaky-wave antenna is set to provoke two resonance frequencies (i.e., PRS and cavity resonances) to converge, thereby diminishing the antenna's broadside directivity. By applying an equivalent circuit model, we explain how a multi-mode resonance (MMR) PRS can adequately be applied to address the underlying challenges. Subsequently, the leaky-wave radiation behavior of an antenna with a heterogeneous substrate is investigated and analytical equations are derived and verified with a full-wave simulation. The effects of material permittivity and thickness in a heterogeneous-cavity antenna on leaky-wave performance are investigated using these approximate yet accurate-enough equations. To justify the findings, two 9 × 9 planar leaky-wave antennas are prototyped on heterogeneous substrates based on SMR and MMR PRS and the radiation performances are compared. Our investigations reveal that in the proposed scenario, an MMR PRS can significantly enhance the antenna's broadside directivity by over 4 dBi at the resonance frequency (27.5 GHz), which is also set to improve radiation pattern compared to a SMR-based antenna. Finally, a single-fed dual-band aperture-shared antenna with a large frequency ratio (S-band and Ka-band) is developed and fabricated on a high-κ substrate based on the proposed MMR PRS.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".