Bibliographic record
Abstract
We have developed a diamond SAW resonator capable of operating at frequencies over 3 GHz using a SiO(2)/ interdigital transducer (IDT)/AlN/diamond structure. This structure is expected to have a high Q value and a zero temperature coefficient of frequency (TCF) over 3 GHz, based on the high acoustic velocity of AlN. The SAW characteristics of various layered structures composed of SiO(2)/IDT/AlN/diamond substrates were studied both theoretically and experimentally. The SiO(2)/IDT/AlN/diamond substrate structure allows for a thicker IDT metal layer compared with other SAW device designs, such as the SiO(2)/IDT/ZnO/diamond structure. The thicker metal IDT in the present design leads to a lower series resistance and, in turn, a low insertion loss for SAW devices over 3 GHz. Using a second-mode (Sezawa-mode) SAW, the phase velocity and electromechanical coupling coefficient of the SiO(2)/IDT/AlN/diamond substrate reached the larger values of 11 150 m/s and 0.5%, respectively, and a zero TCF characteristic at 25°C was achieved. One-port SAW resonators fabricated from diamond substrates showed a high Q of 660 at 5.4 GHz. The frequency drift over a temperature range of -25°C to 80°C was about 90 ppm, even less than that for ST-quartz SAW substrates. A two-port resonator showed a low insertion loss of 8 dB at 5.4 GHz. Finally, we designed a 5-GHz band-stop SAW filter. A 30-MHz-wide stopband at a -6-dB rejection level was achieved while keeping the passband insertion loss to 0.76 dB. These characteristics of these filters show good potential for SHF-band filters.
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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 | 0.001 | 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".