High-isolation antenna array using SIW and realized with a graphene layer for sub-terahertz wireless applications
Bibliographic record
Abstract
Abstract This paper presents the results of a study on developing an effective technique to increase the performance characteristics of antenna arrays for sub-THz integrated circuit applications. This is essential to compensate the limited power available from sub-THz sources. Although conventional array structures can provide a solution to enhance the radiation-gain performance however in the case of small-sized array structures the radiation properties can be adversely affected by mutual coupling that exists between the radiating elements. It is demonstrated here the effectiveness of using SIW technology to suppress surface wave propagations and near field mutual coupling effects. Prototype of 2 × 3 antenna arrays were designed and constructed on a polyimide dielectric substrate with thickness of 125 μm for operation across 0.19–0.20 THz. The dimensions of the array were 20 × 13.5 × 0.125 mm 3 . Metallization of the antenna was coated with 500 nm layer of Graphene. With the proposed technique the isolation between the radiating elements was improved on average by 22.5 dB compared to a reference array antenna with no SIW isolation. The performance of the array was enhanced by transforming the patch to exhibit metamaterial characteristics. This was achieved by embedding the patch antennas in the array with sub-wavelength slots. Compared to the reference array the metamaterial inspired structure exhibits improvement in isolation, radiation gain and efficiency on average by 28 dB, 6.3 dBi, and 34%, respectively. These results show the viability of proposed approach in developing antenna arrays for application in sub-THz integrated circuits.
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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) | 0.001 | 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".