870 000 <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math> </inline-formula>-Factor Capacitive Lamé Mode Resonator With Gap Closing Electrodes Enabling 4.4 k<inline-formula> <tex-math notation="LaTeX">$\Omega$ </tex-math> </inline-formula> Equivalent Resistance at 50 V
Bibliographic record
Abstract
This work presents a Lamé mode resonator featuring a novel gap closing mechanism, which employs electrostatic force to reduce the capacitive transduction gaps to submicrometer values in order to overcome fabrication technology critical spacing limitations. This leads to significant resonator loss and motional resistance reduction while maintaining high Q -factor even in air. Prototypes were fabricated in two commercial silicon-on-insulator processes. Upon the application of a dc voltage of 55 V between the resonator structure and the electrodes, the gaps sizes are reduced to as low as 200 nm. A resonance frequency of 18 MHz with Q -factors as high as 866 000 was observed under 1 mtorr vacuum and as high as 32 000 at atmospheric pressure. A loss of 33 dB was measured at 55 V, which corresponds to an equivalent resistance of 4.4 [Formula: see text], more than 60 times lower than that of a similar design without a gap closing mechanism at the same voltage. This significantly reduces the complexity of the oscillation sustaining circuitry. The frequency tuning range is also increased significantly as a result of the gap reduction, which can be useful for overcoming ambient conditions and fabrication variations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.246 | 0.150 |
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".