Tunable-Efficient Spoof Surface Plasmon Polariton Phase Shifter for 5G and Beyond Applications
Bibliographic record
Abstract
Phase shifters are key components in microwave, millimeter-wave (mm-wave), and optical systems, supporting applications such as beamforming, phased array antennas, RF front ends, and satellite communications. This article introduces a novel tunable phase shifter concept utilizing a highly localized electromagnetic (EM) field in a spoof surface plasmon polariton (SPP) transmission line (TL). Unlike electronic phase shifters, which often face limitations in power handling and insertion loss due to power flowthrough the electronic components such as diodes, or mechanical phase shifters, which typically suffer from bulkiness and higher costs, the proposed concept uniquely combines the advantages of existing technologies such as low insertion loss and high phase-shift range. The proposed design employs one or more dielectric disturbers to dynamically control phase shifts by adjusting their displacement relative to the confined EM field. These disturbers interact with specific numbers of spoof SPP cells, thereby altering the dispersion diagram’s slope. A theoretical analysis was conducted on U-shaped metallic corrugations extended infinitely along they-axis to predict the effect of a movable dielectric disturber on the unit cell’s dispersion characteristics. This behavior was further validated using a planar L-shaped spoof SPP structure, which demonstrated a similar interaction. For instance, when five cells were covered by a movable TMM13 dielectric, a maximum phase shift of 160° was achieved at 28 GHz, with an insertion loss variation of only 0.7 dB. The experimental results confirm the validity of the proposed concept, showing strong agreement with theoretical predictions and simulations. The design’s compatibility with micro-electromechanical systems (MEMS) and piezoelectric actuators facilitates compact and scalable implementations of the proposed phase-shift concept.
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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".