Lamé Resonator Integrated With Chevron-Shaped Thermal Actuators to Improve Motional Resistance and Temperature Stability
Bibliographic record
Abstract
This paper presents a capacitive bulk mode resonator operating in Lamé mode, where the motional resistance and temperature stability are enhanced by implementing a set of chevron-shaped thermal actuators to reduce the transducer air gaps. The chevrons are heated using micro-heaters that are integrated on the actuator. The device is fabricated in the PiezoMUMPS standard microfabrication process by MEMSCAP. The measured resonant frequency for the fabricated device was observed to be 17.9 MHz. It has been experimentally shown that the transducer air gap can be reduced from$2.5 \mu m$to$0.5 \mu m$by applying a heater voltage of 2.7 V at atmospheric pressure. In the proposed resonator, the transmission loss can be reduced by 8.54 dB when the actuators are biased at 1.8 V in vacuum, as opposed to when the actuator is off. Under these conditions, the thermal actuators each consume 39 mA. In addition, the implemented thermal actuators can function as integrated heaters that increase the temperature of the suspended square structure to compensate for ambient temperature variations. As such, applying a voltage of 1.8 V in vacuum reduces the resonant frequency from 17.95 MHz to 17.91 MHz, while applying a voltage of 2.6 V at atmospheric pressure reduces the resonant frequency from 17.97 MHz to 17.86 MHz. [2022-0188]
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".