Small-Signal Equivalent Circuit Model of Long Rectangular CMUT Membranes
Bibliographic record
Abstract
Capacitive micromachined ultrasound transducers (CMUTs) are typically designed with many small membranes per linear array element. However, these membranes can operate out of phase or collapse at different voltages, leading to suboptimal transmit performance and unreliable operation. To avoid these problems, we recently proposed a CMUT architecture with a single large rectangular membrane per element and novel insulated electrode post structures. These single-membrane CMUTs outperformed comparable piezoelectric transducers by almost 3-fold in terms of output pressure and demonstrated electromechanical efficiency values as high as 0.95. In this paper, we present an analytical model which can be used to simulate and optimize single-membrane rectangular CMUTs with or without post structures. Our approach relies on a polynomial deflection model, which was used to derive lumped element model parameters. Using this method, we developed expressions to model both electrostatic and pre-collapse small-signal dynamic CMUT behavior. This modeling framework was incorporated into a MATLAB program. We validated our approach using finite element method (FEM) simulations and experimental results in both air and immersion media. Model predictions for collapse voltage and operating frequency are within 4% of FEM results in both air and immersion. However, the runtime of our MATLAB program was 6 orders of magnitude faster than the corresponding FEM simulations. Compared with experiment, collapse voltage predictions were within 8%, and operating frequency predictions were within 5% in air and 18% in soybean oil. Our results indicate that that rectangular CMUTs may be optimized much further, potentially enabling even greater improvements over piezoelectric transducers.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".