Highly compact filters enabled by robust quarter-wavelength effective localized surface plasmonic resonators
Bibliographic record
Abstract
Abstract This paper presents a design strategy for highly compact filters based on λ g / 4 effective localized surface plasmons (ELSPs). Leveraging the robustness of ELSPs to their cross-sectional shapes, the proposed design facilitates easier integration into printed circuit boards (PCBs). However, challenges such as dielectric material stacking and excessive lateral dimensions remain. By employing λ g / 4 ELSPs, the longitudinal size of the ELSPs-based filters is significantly reduced. Furthermore, embedding the ELSPs directly into the PCB effectively solves the stability problems associated with dielectric material and PCB stacking in conventional dielectric resonator filters, while minimizing the overall filter size. To demonstrate this approach, third-order, fourth-order, and fifth-order ELSPs-based filters were fabricated on a single-layer PCB, achieving center frequencies of 3.6 GHz with fractional bandwidths of 9.3%, 5.1%, and 7.7%, respectively. Experimental results show excellent agreement with simulations. This work provides an ultra-compact filtering solution for next-generation microwave and radio-frequency devices, particularly for applications in 5G and satellite communications.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".