Design, modeling and fabrication of polymer-based Capacitive Micromachined Ultrasonic Transducers (polyCMUTs)
Bibliographic record
Abstract
Ultrasound imaging is the most widely used medical imaging modality in the world. Modern ultrasound systems still rely on the same piezoelectric-based technology since their creation in the 1930s. Despite their mature technology, they are expensive to fabricate, difficult to create 2D arrays and cannot be miniaturized. Capacitive Micromachined Ultrasonic Transducers (CMUTs) are considered the replacement of piezoelectric transducers given their high bandwidth, ease of integration with electronics and miniaturization. The main focus of this dissertation involves the simulation, fabrication and characterization of polymer-based CMUTs (polyCMUTs). A new fabrication process involving inexpensive polymer materials and minimum fabrication steps was developed. The fabrication procedure allows the creation of biocompatible ultrasound chips in a few hours and with costs well below $100 USD, having a performance comparable to current commercial devices. The fabricated polyCMUTs exhibit a phenomenon termed “pre-biasing”, which allowed the operation of polyCMUTs as passive devices (no external power needed). The first B-mode ultrasound image in the world created using polyCMUTs is also presented. As a future plan, the development of a low-cost wearable ultrasound health monitoring system is conceived.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".