Fabrication and testing of polymer-based capacitive micromachined ultrasound transducers for medical imaging
Bibliographic record
Abstract
Abstract The ultrasonic transducer industry is dominated by piezoelectric materials. As an emerging alternative, capacitive micromachined ultrasound transducers (CMUTs) offer wider bandwidth, better integration with electronics, and ease of fabricating large arrays. CMUTs have a sealed cavity between a fixed electrode and a suspended metalized membrane. Manufacturing cost and sensitivity are limiting factors in current CMUTs that depend on the fabrication equipment and, especially, on the materials used. For widespread use of CMUTs, a much lower fabrication cost that uses inexpensive materials, which maintain or improve upon existing sensitivity, is needed. Herein, a new fabrication process is described for polymer-based CMUTs (polyCMUTs) using the photopolymer SU-8 and Omnicoat. The first ultrasound B-mode image of a wire phantom created with a 64-element linear array using synthetic aperture beamforming techniques is presented. A 12 VAC signal superimposed on a 10 VDC signal was used on the transmission side, and only a bias-tee, with no amplifiers, was used on the receiving side. The low operational voltage and high sensitivity of this device can be partially attributed to a pre-biasing condition on the membrane. By using a novel sacrificial layer combined with a top electrode embedded inside the membrane, we demonstrated that SU-8 can be used to manufacture CMUTs inexpensively. Moreover, the fabrication used relatively simple equipment, and the number of fabrication steps was reduced compared to traditional CMUT fabrication. This new fabrication process has the potential to increase the use of CMUTs in the ultrasound market, including the market for wearable 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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".