High-Performance Electrode-Post CMUTs: Fabrication Details and Best Practices
Bibliographic record
Abstract
Capacitive micromachined ultrasound transducers (CMUTs) have been investigated for over 25 years due to their promise for mass manufacturing and electronic co-integration. Previously, CMUTs were fabricated with many small membranes comprising a single transducer element. This, however, resulted in suboptimal electromechanical efficiency and transmit performance, such that resulting devices were not necessarily competitive with piezoelectric transducers. Moreover, many previous CMUT devices were subject to dielectric charging and operational hysteresis that limited long-term reliability. Recently, we demonstrated a CMUT architecture using a single long rectangular membrane per transducer element and novel electrode-post (EP) structures. This architecture not only offers long-term reliability, but also provides performance advantages over previously published CMUT and piezoelectric arrays. The purpose of this article is to highlight these performance advantages and provide details of the fabrication process, including the best practices to avoid common pitfalls. The objective is to provide sufficient detail to inspire a new generation of microfabricated transducers, which could lead to performance gains of future ultrasound systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".