A 6.63-GHz LL-SAW Filter on LiNbO <sub>3</sub> /SiO <sub>2</sub> /SiC With Bandwidth Over 1 GHz
Bibliographic record
Abstract
Surface acoustic wave (SAW) featuring enhanced frequency and large bandwidth (BW) are in high demand for high-throughput wireless communication. This work proposes a low-loss and ultrawideband longitudinal-leaky mode SAW (LL-SAW) filter solution using X-cut lithium niobate (LiNbO3)/SiO2/silicon carbide (SiC) heterosubstrate, specifically aimed at the Wi-Fi 7 6-GHz spectrum. First, the bottlenecks hindering the BW extension of existing LiNbO3/SiC-based LL-SAW filters were analyzed. To address this challenge, the impact of the SiO2intermediate layer on LL-SAW device performance was systematically investigated via simulation. LL-SAW devices were fabricated on both two-layer LiNbO3/SiC and three-layer LiNbO3/SiO2/SiC substrates for performance comparison. LL-SAW resonators on the LiNbO3/SiC substrate exhibit either significantly high loss or strong adjacent spurious modes, severely degrading the filter passband. In contrast, the resonators on the LiNbO3/SiO2/SiC demonstrate scalable resonant frequencies from 5.48 to 7.02 GHz and higher quality factors. The LL-SAW filter on LiNbO3/SiO2/SiC shows a center frequency of 6.629 GHz, a minimum insertion loss (ILmin) of 2.15 dB, an ultrawide 3-dB BW of 1166 MHz, and a peak power exceeding 28 dBm, demonstrating a significant performance improvement compared to the reference sample. The results herein exhibit the potential of LiNbO3/SiO2/SiC heterosubstrate for high-frequency and wideband radio frequency (RF) filters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".