Large-Scale Nonlinear Lumped and Integrated Field Simulations of Top-Orthogonal-to-Bottom-Electrode CMUT Architectures
Bibliographic record
Abstract
Capacitive micromachined ultrasonic transducers (CMUTs) promise many advantages over traditional piezoelectric transducers such as the potential to construct large, cost-effective 2-D arrays. To avoid wiring congestion issues associated with fully wired arrays, top-orthogonal-to-bottom electrode (TOBE) CMUT array architectures have proven to be a more practical alternative, using only 2N wires for an N ×N array. Optimally designing a TOBE CMUT array is a significant challenge due to the range of parameters from the device level up to the operating conditions of the entire array. Since testing many design variations can be prohibitively expensive, a simulation approach accounting for both the small and large-scale array characteristics of TOBE arrays is essential. In this paper, we demonstrate large-scale TOBE CMUT array simulations using a nonlinear CMUT lumped-circuit model. We investigate the performance of the array with different CMUT design parameters and array operating conditions. These simulated results are then compared with measurements of TOBE arrays fabricated using a sacrificial release process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".