Crosstalk and Leakage Suppression by Mode Selectivity and Conversion in Terahertz Hybrid Metallo-Dielectric Waveguide Crossover and Intersections
Bibliographic record
Abstract
Hybrid metallo-dielectric waveguide (HMDW) crossover is proposed and presented in this work. Two parallel metal plates over two intersecting dielectric waveguide (DW) areas create a nonradiative dielectric (NRD) waveguide intersection that is harnessed to avoid the inherent radiation/leakage loss effect of such open DW discontinuities. Straight DW sections far from the intersection are set to minimize conductor loss free from any metal plates. Furthermore, crosstalk can be significantly reduced due to the mode conversion over the NRD waveguide intersection and the mode selectivity of the HMDW architecture. The proposed HMDW crossover has a lower structural insertion loss (material losses are excluded) of 0.37 dB from 275 to 295 GHz, whereas DW crossover and NRD crossover have 1.5 dB of insertion loss. The HMDW architecture is applied to a 10-mm back-to-back alumina-based waveguide with four orthogonal dielectric strips. The metal plates of NRD covering all intersections with a designed width can prevent the EM wave from propagating to four orthogonal dielectric strips. The fabricated prototype has a measured insertion loss of about 5.5 dB from 262 to 286 GHz (4.8 dB in simulation from 262 to 288 GHz). While the hybrid waveguide has a higher loss than its dielectric counterpart, it allows multiple orthogonal guides to cross-pass, providing an alternative solution for integrated systems where intersecting paths are inevitable. Its structural simplicity is beneficial to THz manufacturing.
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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 | 0.000 | 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 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".