Development of a novel configuration for a MEMS transducer for low bias and high resolution imaging applications
Bibliographic record
Abstract
A robust capacitive micromachined ultrasonic transducer has been developed. In this novel configuration, a stack of two deflectable membranes are suspended over a fixed bottom electrode. Similar to conventional capacitive ultrasonic transducers, a generated electrostatic force between the electrodes causes the membranes to deflect and vibrate. However, in this new configuration the transducer effective cavity height is reduced due to the deflection of two membranes. Therefore, the transducer spring constant is more susceptible to bias voltage, which in return reduces the required bias voltage. The transducers have been produced employing a MEMS sacrificial technique where two different membrane anchoring (curved- and flat- anchors) structures, with similar membrane radii were fabricated. Highly doped polysilicon was used as the membrane material. The resonant frequencies of the two transducers have been investigated. It was found that the transducers with curved membrane anchors exhibits a larger resonant frequency shift compared to the transducers with flat membranes for a given bias voltage. Comparison has been made between the spring constant of the flat membrane transducer and that of a conventional single membrane transducer. It is shown that the multiple moving membrane transducer exhibits a larger reduction in the spring constant compared to the conventional transducer, when driven with the same bias voltage. This results in a transducer with a higher power generation capability and sensitivity.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".